Supervised Learning : Characterizing Life Expectancy Drivers Across Countries Using Model-Agnostic Interpretation Methods for Black-Box Models
John Pauline Pineda
July 20, 2023
- 1.
Table of Contents
- 1.1 Introduction
- 1.2 Methodology
- 1.3 Results
- 1.3.1 Data Preparation
- 1.3.2 Data Quality Assessment
- 1.3.3 Data Preprocessing
- 1.3.4 Data Exploration
- 1.3.5 Feature Selection
- 1.3.6 Model Development and Performance Estimation
- 1.3.7 Model Performance Validation
- 1.3.8 Model Selection
- 1.3.9 Model Post-Hoc Analysis
- 1.3.9.1 Dataset Level Exploration : Variable Importance (DLE_VARIMP)
- 1.3.9.2 Dataset Level Exploration : Partial Dependence Plots (DLE_PDP)
- 1.3.9.3 Instance Level Exploration : Breakdown Plots (ILE_BP)
- 1.3.9.4 Instance Level Exploration : Shapley Additive Explanations (ILE_SHAP)
- 1.3.9.5 Instance Level Exploration : Ceteris Paribus Profiles (ILE_CPP)
- 1.3.9.6 Instance Level Exploration : Local Fidelity Plots (ILE_LFP)
- 1.3.9.7 Instance Level Exploration : Local Stability Plots (ILE_LSP)
- 1.4 Summary
- 2. References
1. Table of Contents
Description
1.1 Introduction
1.1.1 Study Objectives
1.1.2 Outcome
1.1.3 Predictors
1.2 Methodology
1.2.1 Feature Selection
Locally Weighted Scatterplot Smoothing Pseudo-R-Squared computes the R-squared statistic - a goodness-of-fit measure which represents explained variability, improvement from null to fitted model and square of the correlation on predicted values obtained from a locally weighted scatterplot smoothing process. LOWESS consists of computing a series of local linear regressions, with each local regression restricted to a window of x-values. Smoothness is achieved by using overlapping windows and by gradually down-weighting points in each regression according to their distance from the anchor point of the window (tri-cube weighting).
Pearson’s Correlation Coefficient is a parametric measure of the linear correlation for a pair of features by calculating the ratio between their covariance and the product of their standard deviations. The presence of high absolute correlation values indicate the univariate association between the numeric predictors and the numeric response.
Spearman’s Rank Correlation Coefficient is a non-parametric measure of the linear correlation for a pair of features by applying the Spearman’s rank equation to the sum of the squared differences between their ranks. The presence of high absolute correlation values indicate the univariate association between the numeric predictors and the numeric response.
Maximal Information Coefficient is an information theory-based measure of two-variable dependence through the computation of the mutual information normalized by the minimum joint entropy. It evaluates the strength of linear or non-linear association using binning as a means to apply mutual information between continuous random variables and selecting the maximum over many possible grids. The presence of high coefficient values indicate the univariate association between the numeric predictors and the numeric response.
Relief Values are heuristic measures which estimate the quality of variables according to how well their values compare to instances that are near to each other, but are efficient in detecting contextual information even with strong dependencies between attributes. Random instances and the corresponding K-nearest instances are selected, with the the weights for the different prediction values, different attributes and different prediction consolidated. The rank of the instance in a sequence of instances ordered by the distance is taken into account based on a a user-defined parameter controlling the influence of the distance. The contributions of each K-nearest instances are normalized by dividing the results with the sum of all K contributions. The presence of high relief values indicate the univariate association between the numeric predictors and the numeric response.
1.2.2 Model Formulation
Stochastic Gradient Boosting is an ensemble learning method which combines multiple weak learners in an additive manner to improve prediction. The process is initialized using a decision tree base learner with the aim of minimizing a specified loss function. The negative gradient of the loss function with respect to the predicted values from the current ensemble is calculated. Residuals are determined as the difference between the actual target values and the learner predictions. A new base learner is subsequently formulated but is trained to predict the residuals. The algorithm involves iteratively improving the ensemble by focusing on the residuals of the previous predictions. Each subsequent base learner is trained to reduce the errors made by the previous ensemble, gradually refining the model’s predictive capabilities.
Random Forest is an ensemble learning method made up of a large set of small decision trees called estimators, with each producing its own prediction. The random forest model aggregates the predictions of the estimators to produce a more accurate prediction. The algorithm involves bootstrap aggregating (where smaller subsets of the training data are repeatedly subsampled with replacement), random subspacing (where a subset of features are sampled and used to train each individual estimator), estimator training (where unpruned decision trees are formulated for each estimator) and inference (where a final prediction is formilated by aggregating the individual predictions of all estimators).
Neural Network comprises of node layers - containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. The process involves the initialization of weights and biases for each neuron in the network. Forward propagation computates the output of the neural network, as determined by the weighted sum of the inputs and a bias term. An activation function is applied to introduce non-linearity to the hidden layer. Back propagation is used to update the weights and the biases in the network by calculating the gradients of the loss function using the chain rule with the magnitude determined by a learning rate. This step allows the network to learn from the errors and adjust the parameters to minimize the loss.
Partial Least Squares applies dimensionality reduction to address high multicollinearity among predictors in a linear regression. The algorithm calculates summary indices termed as partial least squares components which are linear combinations of the original predictors by considering the variation in both the response and the predictor variables. The method of least squares is then applied to fit a linear regression model using the first principal components as predictors, with the optimal number determined using cross-validation.
Cubist Regression is a rule-based model that is an extension of Quinlan’s M5 model tree. A tree is grown where the terminal leaves contain linear regression models. These models are based on the predictors used in previous splits. Also, there are intermediate linear models at each step of the tree. A prediction is made using the linear regression model at the terminal node of the tree, but is smoothed by taking into account the prediction from the linear model in the previous node of the tree (which also occurs recursively up the tree). The tree is reduced to a set of rules, which initially are paths from the top of the tree to the bottom. Rules are eliminated via pruning and/or combined for simplification. The Cubist model can also use a boosting-like scheme called committees where iterative model trees are created in sequence. Another innovation is about using nearest-neighbors to adjust the predictions from the rule-based model.
1.2.3 Model Evaluation
Root Mean Squared Error computes the square root of the average squared difference between the predicted and target values which ranges from zero to infinity. A value of zero indicates perfect prediction of the target values. The metric is weighted according to the square of the error - putting greater influence on large errors than smaller errors which makes it sensitive to outliers but may also encourage conservative prediction.
Median Absolute Deviation computes the median among all absolute difference between the predicted and target values which ranges from zero to infinity. A value of zero indicates perfect prediction of the target values. The metric gives less weight to large errors and is robust to outliers.
R-Squared computes the normalized version of the root mean squared error and also referred to as the coefficient of determination. With a value ranging from zero to infinity, a value of one indicates perfect prediction of the target values. The metric can also be interpreted as the fraction of the total variance in the response variable which can be explained by the model.
1.2.4 Model Post-Hoc Analysis
Variable Importance pertains to model-agnostic methods which allow the comparison of an explanatory variable’s importance between models with different structures. The process involves measuring how much a model’s performance change if the effect of a selected explanatory variable, or of a group of variables, is removed. To remove the effect, perturbations are applied including resampling from an empirical distribution or permutation of the values of the variable. If a variable is important, the model’s performance is expected to worsen after permuting the values of the variable. A larger change in performance indicates the greater importance of the variable.
Partial Dependence Plots show how the expected values of model prediction behave as a function of a selected explanatory variable using the average of a set of individual ceteris paribus profiles. While a ceteris paribus profile shows the dependence of an instance-level prediction on an explanatory variable, a partial dependence profile is estimated by the mean of the ceteris paribus profiles for all instances in a data set.
Breakdown Plots present variable attributions by decomposing the model’s prediction into contributions that can be attributed to the different explanatory variables. Given a prediction which is an approximation of the expected value of the dependent variable driven by the values of explanatory variables, the process involves capturing the contribution of an explanatory variable to the model’s prediction by computing the shift in the expected value of the response, while fixing the values of other variables.
Shapley Additive Explanations are based on Shapley values developed in the cooperative game theory. The process involves explaining a prediction by assuming that each explanatory variable for an instance is a player in a game where the prediction is the payout. The game is the prediction task for a single instance of the data set. The gain is the actual prediction for this instance minus the average prediction for all instances. The players are the explanatory variable values of the instance that collaborate to receive the gain (predict a certain value). The determined value is the average marginal contribution of an explanatory variable across all possible coalitions.
Ceteris Paribus Profiles examine the influence of an explanatory variable by assuming that the values of all other variables do not change. The main objective is to understand how changes in the values of the variable affect the model’s predictions. The process involves evaluating the dependence of the conditional expectation of the response on the values of the particular explanatory variable.
Local Fidelity Plots evaluate the local predictive performance of the model around the observation of interest. The process involves summarizing two distributions of residuals including the residuals for the neighbors of the observation of interest and residuals for the entire training dataset except for neighbors. The results help evaluate whether the model-fit for the instance of interest is unbiased (based on small residuals with distributions symmetric around 0).
Local Stability Plots assess the local stability of predictions around the observation of interest. The process involves checking whether small changes in the explanatory variables, as represented by the changes within the set of neighbors, induce much influence on the predictions. The results help evaluate whether the model is locally additive (based on parallel ceteris paribus profiles) and locally stable (based on adjacent ceteris paribus profiles).
1.3 Results
1.3.1 Data Preparation
[A] The initial tabular dataset was comprised of 394 observations and 23 variables (including 2 metadata, 1 response and 20 predictors).
[A.1] 394 rows (observations)
[A.2] 23 columns (variables)
[A.2.1] 1/23 instance labels = COUNTRY variable (character)
[A.2.2] 1/23 supplementary information = YEAR variable (numeric)
[A.2.3] 1/23 response = LIFEXP variable (numeric)
[A.2.4] 20/23 predictors = 18/20 numeric + 2/20 factor
[A.2.4.1] GENDER (factor)
[A.2.4.2] CONTIN (factor)
[A.2.4.3] UNEMPR (numeric)
[A.2.4.4] INFMOR (numeric)
[A.2.4.5] GDP (numeric)
[A.2.4.6] GNI (numeric)
[A.2.4.7] CLTECH (numeric)
[A.2.4.8] PERCAP (numeric)
[A.2.4.9] RTIMOR (numeric)
[A.2.4.10] TUBINC (numeric)
[A.2.4.11] DPTIMM (numeric)
[A.2.4.12] HEPIMM (numeric)
[A.2.4.13] MEAIMM (numeric)
[A.2.4.14] HOSBED (numeric)
[A.2.4.15] SANSER (numeric)
[A.2.4.16] TUBTRT (numeric)
[A.2.4.17] URBPOP (numeric)
[A.2.4.18] RURPOP (numeric)
[A.2.4.19] NCOMOR (numeric)
[A.2.4.20] SUIRAT (numeric)
[B] Preliminary transformation was applied to the GDP, GNI and PERCAP variables noted with high ranges.
[B.1] GDP (numeric)
[B.2] GNI (numeric)
[B.3] PERCAP (numeric)
Code Chunk | Output
##################################
# Loading R libraries
##################################
library(DALEX)
library(caret)
library(randomForest)
library(e1071)
library(gbm)
library(skimr)
library(corrplot)
library(lares)
library(dplyr)
library(minerva)
library(CORElearn)
library(patchwork)
library(lime)
library(DALEXtra)
##################################
# Loading source and
# formulating the analysis set
##################################
LED <- read.csv("Life_Expectancy_Data.csv",
na.strings=c("NA","NaN"," ",""),
stringsAsFactors = FALSE)
LED <- as.data.frame(LED)
##################################
# Performing a general exploration of the data set
##################################
dim(LED)## [1] 394 23str(LED)## 'data.frame': 394 obs. of 23 variables:
## $ COUNTRY: chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ YEAR : int 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 ...
## $ GENDER : chr "Female" "Female" "Female" "Female" ...
## $ CONTIN : chr "Asia" "Europe" "Africa" "Africa" ...
## $ LIFEXP : num 66.4 80.2 78.1 64 78.1 ...
## $ UNEMPR : num 14.06 11.32 18.63 7.84 8.26 ...
## $ INFMOR : num 42.9 7.7 18.6 44.5 5.1 ...
## $ GDP : num 1.88e+10 1.54e+10 1.72e+11 8.94e+10 1.69e+09 ...
## $ GNI : num 1.91e+10 1.52e+10 1.68e+11 8.19e+10 1.58e+09 ...
## $ CLTECH : num 36 80.7 99.3 49.6 100 ...
## $ PERCAP : num 494 5396 3990 2810 17377 ...
## $ RTIMOR : num 15.9 11.7 20.9 26.1 0 ...
## $ TUBINC : num 189 16 61 351 0 29 26 2.2 6.9 6 ...
## $ DPTIMM : num 66 99 91 57 95 ...
## $ HEPIMM : num 66 99 91 53 99 ...
## $ MEAIMM : num 64 95 80 51 93 ...
## $ HOSBED : num 0.432 3.052 1.8 0.8 2.581 ...
## $ SANSER : num 49 99.2 86.1 51.4 85.5 ...
## $ TUBTRT : num 91 88 86 69 72.3 ...
## $ URBPOP : num 25.8 61.2 73.2 66.2 24.5 ...
## $ RURPOP : num 74.2 38.8 26.8 33.8 75.5 ...
## $ NCOMOR : num 36.2 6 12.8 19.4 17.6 ...
## $ SUIRAT : num 3.6 2.7 1.8 2.3 0.8 ...summary(LED)## COUNTRY YEAR GENDER CONTIN
## Length:394 Min. :2019 Length:394 Length:394
## Class :character 1st Qu.:2019 Class :character Class :character
## Mode :character Median :2019 Mode :character Mode :character
## Mean :2019
## 3rd Qu.:2019
## Max. :2019
## LIFEXP UNEMPR INFMOR GDP
## Min. :51.20 Min. : 0.071 Min. : 1.40 Min. :1.880e+08
## 1st Qu.:67.61 1st Qu.: 3.560 1st Qu.: 6.00 1st Qu.:1.130e+10
## Median :74.33 Median : 5.663 Median :15.20 Median :3.865e+10
## Mean :73.07 Mean : 7.769 Mean :21.55 Mean :4.614e+11
## 3rd Qu.:79.30 3rd Qu.: 9.842 3rd Qu.:30.66 3rd Qu.:2.450e+11
## Max. :88.10 Max. :41.153 Max. :88.80 Max. :2.140e+13
## GNI CLTECH PERCAP RTIMOR
## Min. :3.754e+08 Min. : 0.00 Min. : 228.2 Min. : 0.0
## 1st Qu.:1.111e+10 1st Qu.: 33.50 1st Qu.: 2229.9 1st Qu.: 8.2
## Median :4.005e+10 Median : 79.50 Median : 6609.5 Median :16.0
## Mean :4.814e+11 Mean : 65.66 Mean : 16682.2 Mean :17.0
## 3rd Qu.:2.450e+11 3rd Qu.:100.00 3rd Qu.: 19303.5 3rd Qu.:23.9
## Max. :2.170e+13 Max. :100.00 Max. :175813.9 Max. :64.6
## TUBINC DPTIMM HEPIMM MEAIMM
## Min. : 0.0 Min. :35.00 Min. :35.00 Min. :37.00
## 1st Qu.: 12.0 1st Qu.:85.69 1st Qu.:81.31 1st Qu.:84.85
## Median : 46.0 Median :92.00 Median :91.00 Median :92.00
## Mean :103.5 Mean :87.87 Mean :86.64 Mean :87.21
## 3rd Qu.:140.0 3rd Qu.:97.00 3rd Qu.:96.00 3rd Qu.:96.00
## Max. :654.0 Max. :99.00 Max. :99.00 Max. :99.00
## HOSBED SANSER TUBTRT URBPOP
## Min. : 0.200 Min. : 8.632 Min. : 0.00 Min. : 13.25
## 1st Qu.: 1.300 1st Qu.: 63.898 1st Qu.: 73.00 1st Qu.: 41.61
## Median : 2.570 Median : 91.144 Median : 82.00 Median : 58.76
## Mean : 2.987 Mean : 77.495 Mean : 77.68 Mean : 59.09
## 3rd Qu.: 3.746 3rd Qu.: 98.516 3rd Qu.: 88.00 3rd Qu.: 77.94
## Max. :13.710 Max. :100.000 Max. :100.00 Max. :100.00
## RURPOP NCOMOR SUIRAT
## Min. : 0.00 Min. : 4.40 Min. : 0.000
## 1st Qu.:22.06 1st Qu.:13.60 1st Qu.: 3.300
## Median :41.24 Median :19.95 Median : 6.850
## Mean :40.91 Mean :20.02 Mean : 9.572
## 3rd Qu.:58.39 3rd Qu.:24.07 3rd Qu.: 11.175
## Max. :86.75 Max. :58.40 Max. :116.000##################################
# Transforming to appropriate data types
##################################
LED$YEAR <- factor(LED$YEAR,
levels = c("2019"))
LED$GENDER <- factor(LED$GENDER,
levels = c("Male","Female"))
LED$CONTIN <- as.factor(LED$CONTIN)
##################################
# Reducing the range of values
# for certain numeric predictors
##################################
LED$GDP <- LED$GDP/1000000000
LED$GNI <- LED$GNI/1000000000
LED$PERCAP <- LED$PERCAP/1000
##################################
# Formulating a data type assessment summary
##################################
PDA <- LED
(PDA.Summary <- data.frame(
Column.Index=c(1:length(names(PDA))),
Column.Name= names(PDA),
Column.Type=sapply(PDA, function(x) class(x)),
row.names=NULL)
)## Column.Index Column.Name Column.Type
## 1 1 COUNTRY character
## 2 2 YEAR factor
## 3 3 GENDER factor
## 4 4 CONTIN factor
## 5 5 LIFEXP numeric
## 6 6 UNEMPR numeric
## 7 7 INFMOR numeric
## 8 8 GDP numeric
## 9 9 GNI numeric
## 10 10 CLTECH numeric
## 11 11 PERCAP numeric
## 12 12 RTIMOR numeric
## 13 13 TUBINC numeric
## 14 14 DPTIMM numeric
## 15 15 HEPIMM numeric
## 16 16 MEAIMM numeric
## 17 17 HOSBED numeric
## 18 18 SANSER numeric
## 19 19 TUBTRT numeric
## 20 20 URBPOP numeric
## 21 21 RURPOP numeric
## 22 22 NCOMOR numeric
## 23 23 SUIRAT numeric1.3.2 Data Quality Assessment
[A] No missing observations noted for any variable.
[B] Low variance observed for 3 numeric predictors with First.Second.Mode.Ratio>5.
[B.1] SANSER = 12.00
[B.2] UNEMPR = 11.00
[B.3] NCOMOR = 6.00
[C] No low variance observed for any predictor with Unique.Count.Ratio<0.01.
[D] High skewness observed for 3 numeric predictors with Skewness>3 or Skewness<(-3).
[D.1] GDP = 8.62
[D.2] GNI = 8.55
[D.3] SUIRAT = 4.08
Code Chunk | Output
##################################
# Loading dataset
##################################
DQA <- LED
##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
Column.Index=c(1:length(names(DQA))),
Column.Name= names(DQA),
Column.Type=sapply(DQA, function(x) class(x)),
Row.Count=sapply(DQA, function(x) nrow(DQA)),
NA.Count=sapply(DQA,function(x)sum(is.na(x))),
Fill.Rate=sapply(DQA,function(x)format(round((sum(!is.na(x))/nrow(DQA)),3),nsmall=3)),
row.names=NULL)
)## Column.Index Column.Name Column.Type Row.Count NA.Count Fill.Rate
## 1 1 COUNTRY character 394 0 1.000
## 2 2 YEAR factor 394 0 1.000
## 3 3 GENDER factor 394 0 1.000
## 4 4 CONTIN factor 394 0 1.000
## 5 5 LIFEXP numeric 394 0 1.000
## 6 6 UNEMPR numeric 394 0 1.000
## 7 7 INFMOR numeric 394 0 1.000
## 8 8 GDP numeric 394 0 1.000
## 9 9 GNI numeric 394 0 1.000
## 10 10 CLTECH numeric 394 0 1.000
## 11 11 PERCAP numeric 394 0 1.000
## 12 12 RTIMOR numeric 394 0 1.000
## 13 13 TUBINC numeric 394 0 1.000
## 14 14 DPTIMM numeric 394 0 1.000
## 15 15 HEPIMM numeric 394 0 1.000
## 16 16 MEAIMM numeric 394 0 1.000
## 17 17 HOSBED numeric 394 0 1.000
## 18 18 SANSER numeric 394 0 1.000
## 19 19 TUBTRT numeric 394 0 1.000
## 20 20 URBPOP numeric 394 0 1.000
## 21 21 RURPOP numeric 394 0 1.000
## 22 22 NCOMOR numeric 394 0 1.000
## 23 23 SUIRAT numeric 394 0 1.000##################################
# Listing all Predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric Predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,sapply(DQA.Predictors, is.numeric), drop = FALSE]
if (length(names(DQA.Predictors.Numeric))>0) {
print(paste0("There is (are) ",
(length(names(DQA.Predictors.Numeric))),
" numeric descriptor variable(s)."))
} else {
print("There are no numeric descriptor variables.")
}## [1] "There is (are) 18 numeric descriptor variable(s)."##################################
# Listing all factor Predictors
##################################
DQA.Predictors.Factor <- DQA.Predictors[,sapply(DQA.Predictors, is.factor), drop = FALSE]
if (length(names(DQA.Predictors.Factor))>0) {
print(paste0("There is (are) ",
(length(names(DQA.Predictors.Factor))),
" factor descriptor variable(s)."))
} else {
print("There are no factor descriptor variables.")
}## [1] "There is (are) 2 factor descriptor variable(s)."##################################
# Formulating a data quality assessment summary for factor Predictors
##################################
if (length(names(DQA.Predictors.Factor))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = x[!(x %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return("x"),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Factor.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Factor),
Column.Type=sapply(DQA.Predictors.Factor, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Factor, function(x) length(unique(x))),
First.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(FirstModes(x)[1])),
Second.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(SecondModes(x)[1])),
First.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
Unique.Count.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Factor)),3), nsmall=3)),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1 GENDER factor 2 Female x
## 2 CONTIN factor 6 Africa Asia
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1 197 0 0.005 Inf
## 2 106 100 0.015 1.060##################################
# Formulating a data quality assessment summary for numeric Predictors
##################################
if (length(names(DQA.Predictors.Numeric))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = na.omit(x)[!(na.omit(x) %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return(0.00001),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Numeric.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Numeric),
Column.Type=sapply(DQA.Predictors.Numeric, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Numeric, function(x) length(unique(x))),
Unique.Count.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Numeric)),3), nsmall=3)),
First.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
Second.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
First.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
Minimum=sapply(DQA.Predictors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
Mean=sapply(DQA.Predictors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
Median=sapply(DQA.Predictors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
Maximum=sapply(DQA.Predictors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
Skewness=sapply(DQA.Predictors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
Kurtosis=sapply(DQA.Predictors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
Percentile25th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
Percentile75th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1 UNEMPR numeric 370 0.939 8.256
## 2 INFMOR numeric 245 0.622 30.235
## 3 GDP numeric 254 0.645 303.000
## 4 GNI numeric 253 0.642 2040.000
## 5 CLTECH numeric 112 0.284 100.000
## 6 PERCAP numeric 196 0.497 12.669
## 7 RTIMOR numeric 141 0.358 18.229
## 8 TUBINC numeric 146 0.371 136.043
## 9 DPTIMM numeric 45 0.114 99.000
## 10 HEPIMM numeric 45 0.114 81.308
## 11 MEAIMM numeric 48 0.122 99.000
## 12 HOSBED numeric 173 0.439 2.986
## 13 SANSER numeric 186 0.472 100.000
## 14 TUBTRT numeric 59 0.150 84.000
## 15 URBPOP numeric 191 0.485 100.000
## 16 RURPOP numeric 191 0.485 0.000
## 17 NCOMOR numeric 214 0.543 22.100
## 18 SUIRAT numeric 176 0.447 10.619
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1 3.924 22 2 11.000
## 2 2.100 28 7 4.000
## 3 279.000 4 3 1.333
## 4 316.000 8 4 2.000
## 5 60.593 108 34 3.176
## 6 0.494 4 2 2.000
## 7 26.800 28 6 4.667
## 8 35.000 12 10 1.200
## 9 85.685 44 30 1.467
## 10 99.000 40 38 1.053
## 11 84.855 48 30 1.600
## 12 0.400 34 8 4.250
## 13 49.006 24 2 12.000
## 14 83.000 22 20 1.100
## 15 55.985 10 4 2.500
## 16 44.015 10 4 2.500
## 17 6.800 30 5 6.000
## 18 7.600 30 8 3.750
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 1 0.071 7.769 5.663 41.153 1.751 3.680 3.560
## 2 1.400 21.546 15.200 88.800 1.084 0.567 6.000
## 3 0.188 461.374 38.653 21400.000 8.619 82.715 11.304
## 4 0.375 481.441 40.048 21700.000 8.547 82.139 11.110
## 5 0.000 65.660 79.500 100.000 -0.623 -1.141 33.500
## 6 0.228 16.682 6.610 175.814 2.810 10.880 2.230
## 7 0.000 17.003 16.000 64.600 0.740 1.028 8.200
## 8 0.000 103.489 46.000 654.000 1.864 3.177 12.000
## 9 35.000 87.875 92.000 99.000 -1.856 3.434 85.685
## 10 35.000 86.640 91.000 99.000 -1.595 2.477 81.308
## 11 37.000 87.207 92.000 99.000 -1.688 2.574 84.855
## 12 0.200 2.987 2.570 13.710 1.697 3.859 1.300
## 13 8.632 77.495 91.144 100.000 -1.122 -0.155 63.898
## 14 0.000 77.675 82.000 100.000 -2.194 5.571 73.000
## 15 13.250 59.094 58.760 100.000 -0.132 -0.991 41.612
## 16 0.000 40.906 41.240 86.750 0.132 -0.991 22.058
## 17 4.400 20.021 19.950 58.400 0.864 1.551 13.600
## 18 0.000 9.572 6.850 116.000 4.082 29.352 3.300
## Percentile75th
## 1 9.842
## 2 30.659
## 3 245.000
## 4 245.000
## 5 100.000
## 6 19.304
## 7 23.900
## 8 140.000
## 9 97.000
## 10 96.000
## 11 96.000
## 12 3.746
## 13 98.516
## 14 88.000
## 15 77.942
## 16 58.388
## 17 24.075
## 18 11.175##################################
# Identifying potential data quality issues
##################################
##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
print(paste0("Missing observations noted for ",
(nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
" variable(s) with NA.Count>0 and Fill.Rate<1.0."))
DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
print("No missing observations noted.")
}## [1] "No missing observations noted."##################################
# Checking for zero or near-zero variance Predictors
##################################
if (length(names(DQA.Predictors.Factor))==0) {
print("No factor Predictors noted.")
} else if (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
" factor variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance factor Predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 1 factor variable(s) with First.Second.Mode.Ratio>5."## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1 GENDER factor 2 Female x
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio First.Second.Mode.Ratio
## 1 197 0 0.005 Infif (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
" numeric variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance numeric Predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 3 numeric variable(s) with First.Second.Mode.Ratio>5."## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 1 UNEMPR numeric 370 0.939 8.256
## 13 SANSER numeric 186 0.472 100.000
## 17 NCOMOR numeric 214 0.543 22.100
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 1 3.924 22 2 11.000
## 13 49.006 24 2 12.000
## 17 6.800 30 5 6.000
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 1 0.071 7.769 5.663 41.153 1.751 3.680 3.560
## 13 8.632 77.495 91.144 100.000 -1.122 -0.155 63.898
## 17 4.400 20.021 19.950 58.400 0.864 1.551 13.600
## Percentile75th
## 1 9.842
## 13 98.516
## 17 24.075if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])),
" numeric variable(s) with Unique.Count.Ratio<0.01."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,]
} else {
print("No low variance numeric Predictors due to low unique count ratio noted.")
}## [1] "No low variance numeric Predictors due to low unique count ratio noted."##################################
# Checking for skewed Predictors
##################################
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric Predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])>0){
print(paste0("High skewness observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])),
" numeric variable(s) with Skewness>3 or Skewness<(-3)."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),]
} else {
print("No skewed numeric Predictors noted.")
}## [1] "High skewness observed for 3 numeric variable(s) with Skewness>3 or Skewness<(-3)."## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 3 GDP numeric 254 0.645 303.000
## 4 GNI numeric 253 0.642 2040.000
## 18 SUIRAT numeric 176 0.447 10.619
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 3 279.000 4 3 1.333
## 4 316.000 8 4 2.000
## 18 7.600 30 8 3.750
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th
## 3 0.188 461.374 38.653 21400.000 8.619 82.715 11.304
## 4 0.375 481.441 40.048 21700.000 8.547 82.139 11.110
## 18 0.000 9.572 6.850 116.000 4.082 29.352 3.300
## Percentile75th
## 3 245.000
## 4 245.000
## 18 11.1751.3.3 Data Preprocessing
1.3.3.1 Outlier Treatment
[A] Outliers noted for 15 out of the 18 numeric predictors. Predictor values were visualized through a boxplot including observations classified as suspected outliers using the IQR criterion. The IQR criterion means that all observations above the (75th percentile + 1.5 x IQR) or below the (25th percentile - 1.5 x IQR) are suspected outliers, where IQR is the difference between the third quartile (75th percentile) and first quartile (25th percentile).
[A.1] UNEMPR = 30
[A.2] INFMOR = 10
[A.3] GDP = 46
[A.4] GNI = 48
[A.5] PERCAP = 46
[A.6] RTIMOR = 2
[A.7] TUBINC = 32
[A.8] DPTIMM = 34
[A.9] HEPIMM = 26
[A.10] MEAIMM = 40
[A.11] HOSBED = 16
[A.12] SANSER = 8
[A.13] TUBTRT = 34
[A.14] NCOMOR = 11
[A.15] SUIRAT = 29
[B] Distributional anomalies observed for 9 predictors showing a high number of observations reporting the exact same set of values.
[B.1] INFMOR=30.20
[B.2] CLTECH=60.60
[B.3] RTIMOR=18.20
[B.4] DPTIMM=85.70
[B.5] HEPIMM=81.30
[B.6] MEAIMM=84.90
[B.7] HOSBED=3.00
[B.8] NCOMOR=22.10
[B.9] SUIRAT=10.60
[C] A total of 30 observations representing 15 countries associated with these unreliable values were removed for the subsequent analysis.
[C.1] COUNTRY=Aruba
[C.2] COUNTRY=Bermuda
[C.3] COUNTRY=Channel Islands
[C.4] COUNTRY=Faroe Islands
[C.5] COUNTRY=French Polynesia
[C.6] COUNTRY=Guam
[C.7] COUNTRY=Hong Kong SAR, China
[C.8] COUNTRY=Kosovo
[C.9] COUNTRY=Liechtenstein
[C.10] COUNTRY=Macao SAR, China
[C.11] COUNTRY=New Caledonia
[C.12] COUNTRY=Puerto Rico
[C.13] COUNTRY=St. Martin (French part)
[C.14] COUNTRY=Virgin Islands (U.S.)
[C.15] COUNTRY=West Bank and Gaza
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- LED
##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA))| Name | DPA |
| Number of rows | 394 |
| Number of columns | 23 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 3 |
| numeric | 19 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| COUNTRY | 0 | 1 | 4 | 30 | 0 | 197 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| YEAR | 0 | 1 | FALSE | 1 | 201: 394 |
| GENDER | 0 | 1 | FALSE | 2 | Mal: 197, Fem: 197 |
| CONTIN | 0 | 1 | FALSE | 6 | Afr: 106, Asi: 100, Eur: 86, Nor: 52 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| LIFEXP | 0 | 1 | 73.07 | 7.82 | 51.20 | 67.61 | 74.32 | 79.30 | 88.10 | ▁▃▆▇▃ |
| UNEMPR | 0 | 1 | 7.77 | 6.35 | 0.07 | 3.56 | 5.66 | 9.84 | 41.15 | ▇▂▁▁▁ |
| INFMOR | 0 | 1 | 21.55 | 18.67 | 1.40 | 6.00 | 15.20 | 30.66 | 88.80 | ▇▃▂▁▁ |
| GDP | 0 | 1 | 461.37 | 1920.36 | 0.19 | 11.30 | 38.65 | 245.00 | 21400.00 | ▇▁▁▁▁ |
| GNI | 0 | 1 | 481.44 | 1942.86 | 0.38 | 11.11 | 40.05 | 245.00 | 21700.00 | ▇▁▁▁▁ |
| CLTECH | 0 | 1 | 65.66 | 36.33 | 0.00 | 33.50 | 79.50 | 100.00 | 100.00 | ▃▁▁▂▇ |
| PERCAP | 0 | 1 | 16.68 | 24.32 | 0.23 | 2.23 | 6.61 | 19.30 | 175.81 | ▇▁▁▁▁ |
| RTIMOR | 0 | 1 | 17.00 | 10.34 | 0.00 | 8.20 | 16.00 | 23.90 | 64.60 | ▇▇▅▁▁ |
| TUBINC | 0 | 1 | 103.49 | 133.68 | 0.00 | 12.00 | 46.00 | 140.00 | 654.00 | ▇▂▁▁▁ |
| DPTIMM | 0 | 1 | 87.87 | 12.41 | 35.00 | 85.69 | 92.00 | 97.00 | 99.00 | ▁▁▁▃▇ |
| HEPIMM | 0 | 1 | 86.64 | 12.72 | 35.00 | 81.31 | 91.00 | 96.00 | 99.00 | ▁▁▁▃▇ |
| MEAIMM | 0 | 1 | 87.21 | 13.17 | 37.00 | 84.85 | 92.00 | 96.00 | 99.00 | ▁▁▁▃▇ |
| HOSBED | 0 | 1 | 2.99 | 2.35 | 0.20 | 1.30 | 2.57 | 3.75 | 13.71 | ▇▅▂▁▁ |
| SANSER | 0 | 1 | 77.49 | 27.63 | 8.63 | 63.90 | 91.14 | 98.52 | 100.00 | ▁▁▁▂▇ |
| TUBTRT | 0 | 1 | 77.68 | 16.97 | 0.00 | 73.00 | 82.00 | 88.00 | 100.00 | ▁▁▁▅▇ |
| URBPOP | 0 | 1 | 59.09 | 23.24 | 13.25 | 41.61 | 58.76 | 77.94 | 100.00 | ▅▆▇▇▆ |
| RURPOP | 0 | 1 | 40.91 | 23.24 | 0.00 | 22.06 | 41.24 | 58.39 | 86.75 | ▆▇▇▆▅ |
| NCOMOR | 0 | 1 | 20.02 | 8.40 | 4.40 | 13.60 | 19.95 | 24.08 | 58.40 | ▅▇▂▁▁ |
| SUIRAT | 0 | 1 | 9.57 | 10.49 | 0.00 | 3.30 | 6.85 | 11.17 | 116.00 | ▇▁▁▁▁ |
##################################
# Outlier Treatment
##################################
##################################
# Listing all Predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Outliers <- boxplot.stats(DPA.Predictors.Numeric[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}
##################################
# Formulating the histogram
# for the numeric predictors
##################################
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Median <- format(round(median(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
Mean <- format(round(mean(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
Skewness <- format(round(skewness(DPA.Predictors.Numeric[,i],na.rm = TRUE),2), nsmall=2)
print(
ggplot(DPA.Predictors.Numeric, aes(x=DPA.Predictors.Numeric[,i])) +
geom_histogram(binwidth=1,color="black", fill="white") +
geom_vline(aes(xintercept=mean(DPA.Predictors.Numeric[,i])),
color="blue", size=1) +
geom_vline(aes(xintercept=median(DPA.Predictors.Numeric[,i])),
color="red", size=1) +
theme_bw() +
ylab("Count") +
xlab(names(DPA.Predictors.Numeric)[i]) +
labs(title=names(DPA.Predictors.Numeric)[i],
subtitle=paste0("Median = ", Median,
", Mean = ", Mean,
", Skewness = ", Skewness)))
}
##################################
# Investigating distributional anomalies
# observed for several predictors
##################################
(INFMOR_Unique <- DPA %>%
group_by(INFMOR) %>%
summarize(Distinct_INFMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_INFMOR)) %>%
slice(1:5))## # A tibble: 5 x 2
## INFMOR Distinct_INFMOR
## <dbl> <int>
## 1 30.2 14
## 2 2.1 7
## 3 6.4 6
## 4 1.7 4
## 5 2.5 4(INFMOR_Unique_Country <- DPA[round(DPA$INFMOR,digits=1)==30.2,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "Aruba" "Bermuda"
## [17] "Channel Islands" "Faroe Islands"
## [19] "French Polynesia" "Guam"
## [21] "Hong Kong SAR, China" "Kosovo"
## [23] "Liechtenstein" "Macao SAR, China"
## [25] "New Caledonia" "Puerto Rico"
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"DPA %>%
group_by(CLTECH) %>%
summarize(Distinct_CLTECH = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_CLTECH)) %>%
slice(1:5)## # A tibble: 5 x 2
## CLTECH Distinct_CLTECH
## <dbl> <int>
## 1 100 54
## 2 60.6 17
## 3 9.30 3
## 4 99.9 3
## 5 0.2 2(CLTECH_Unique_Country <- DPA[round(DPA$CLTECH,digits=1)==60.6,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Lebanon" "Libya"
## [11] "Liechtenstein" "Macao SAR, China"
## [13] "New Caledonia" "Puerto Rico"
## [15] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [17] "West Bank and Gaza" "Aruba"
## [19] "Bermuda" "Channel Islands"
## [21] "Faroe Islands" "French Polynesia"
## [23] "Guam" "Hong Kong SAR, China"
## [25] "Kosovo" "Lebanon"
## [27] "Libya" "Liechtenstein"
## [29] "Macao SAR, China" "New Caledonia"
## [31] "Puerto Rico" "St. Martin (French part)"
## [33] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(RTIMOR) %>%
summarize(Distinct_RTIMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_RTIMOR)) %>%
slice(1:5)## # A tibble: 5 x 2
## RTIMOR Distinct_RTIMOR
## <dbl> <int>
## 1 18.2 14
## 2 3.9 3
## 3 5.1 3
## 4 5.3 3
## 5 12.7 3(RTIMOR_Unique_Country <- DPA[round(DPA$RTIMOR,digits=1)==18.2,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "Aruba" "Bermuda"
## [17] "Channel Islands" "Faroe Islands"
## [19] "French Polynesia" "Guam"
## [21] "Hong Kong SAR, China" "Kosovo"
## [23] "Liechtenstein" "Macao SAR, China"
## [25] "New Caledonia" "Puerto Rico"
## [27] "St. Martin (French part)" "Virgin Islands (U.S.)"DPA %>%
group_by(DPTIMM) %>%
summarize(Distinct_DPTIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_DPTIMM)) %>%
slice(1:5)## # A tibble: 5 x 2
## DPTIMM Distinct_DPTIMM
## <dbl> <int>
## 1 99 22
## 2 85.7 15
## 3 97 14
## 4 98 14
## 5 95 13(DPTIMM_Unique_Country <- DPA[round(DPA$DPTIMM,digits=1)==85.7,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(HEPIMM) %>%
summarize(Distinct_HEPIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_HEPIMM)) %>%
slice(1:5)## # A tibble: 5 x 2
## HEPIMM Distinct_HEPIMM
## <dbl> <int>
## 1 81.3 20
## 2 99 19
## 3 97 17
## 4 98 11
## 5 92 10(HEPIMM_Unique_Country <- DPA[round(DPA$HEPIMM,digits=1)==81.3,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Denmark"
## [5] "Faroe Islands" "Finland"
## [7] "French Polynesia" "Guam"
## [9] "Hong Kong SAR, China" "Hungary"
## [11] "Iceland" "Kosovo"
## [13] "Liechtenstein" "Macao SAR, China"
## [15] "New Caledonia" "Puerto Rico"
## [17] "Slovenia" "St. Martin (French part)"
## [19] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [21] "Aruba" "Bermuda"
## [23] "Channel Islands" "Denmark"
## [25] "Faroe Islands" "Finland"
## [27] "French Polynesia" "Guam"
## [29] "Hong Kong SAR, China" "Hungary"
## [31] "Iceland" "Kosovo"
## [33] "Liechtenstein" "Macao SAR, China"
## [35] "New Caledonia" "Puerto Rico"
## [37] "Slovenia" "St. Martin (French part)"
## [39] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(MEAIMM) %>%
summarize(Distinct_MEAIMM = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_MEAIMM)) %>%
slice(1:5)## # A tibble: 5 x 2
## MEAIMM Distinct_MEAIMM
## <dbl> <int>
## 1 99 24
## 2 84.9 15
## 3 95 14
## 4 96 14
## 5 98 13(MEAIMM_Unique_Country <- DPA[round(DPA$MEAIMM,digits=1)==84.9,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(HOSBED) %>%
summarize(Distinct_HOSBED = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_HOSBED)) %>%
slice(1:5)## # A tibble: 5 x 2
## HOSBED Distinct_HOSBED
## <dbl> <int>
## 1 2.99 17
## 2 0.4 4
## 3 0.8 2
## 4 0.85 2
## 5 0.9 2(HOSBED_Unique_Country <- DPA[round(DPA$HOSBED,digits=1)==3.0,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "Namibia" "New Caledonia"
## [13] "Papua New Guinea" "Puerto Rico"
## [15] "South Sudan" "St. Martin (French part)"
## [17] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [19] "Aruba" "Bermuda"
## [21] "Channel Islands" "Faroe Islands"
## [23] "French Polynesia" "Guam"
## [25] "Hong Kong SAR, China" "Kosovo"
## [27] "Liechtenstein" "Macao SAR, China"
## [29] "Namibia" "New Caledonia"
## [31] "Papua New Guinea" "Puerto Rico"
## [33] "South Sudan" "St. Martin (French part)"
## [35] "Virgin Islands (U.S.)" "West Bank and Gaza"DPA %>%
group_by(NCOMOR) %>%
summarize(Distinct_NCOMOR = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_NCOMOR)) %>%
slice(1:5)## # A tibble: 5 x 2
## NCOMOR Distinct_NCOMOR
## <dbl> <int>
## 1 22.1 15
## 2 6.8 5
## 3 13.6 5
## 4 15.2 5
## 5 17.5 5(NCOMOR_Unique_Country <- DPA[round(DPA$NCOMOR,digits=1)==22.1,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Burkina Faso" "Channel Islands"
## [5] "Faroe Islands" "French Polynesia"
## [7] "Guam" "Hong Kong SAR, China"
## [9] "Kosovo" "Liechtenstein"
## [11] "Macao SAR, China" "New Caledonia"
## [13] "Puerto Rico" "St. Martin (French part)"
## [15] "Virgin Islands (U.S.)" "West Bank and Gaza"
## [17] "Aruba" "Bermuda"
## [19] "Channel Islands" "Dominican Republic"
## [21] "Equatorial Guinea" "Estonia"
## [23] "Faroe Islands" "French Polynesia"
## [25] "Guam" "Hong Kong SAR, China"
## [27] "Kosovo" "Liechtenstein"
## [29] "Macao SAR, China" "New Caledonia"
## [31] "Puerto Rico" "Sierra Leone"
## [33] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [35] "West Bank and Gaza"DPA %>%
group_by(SUIRAT) %>%
summarize(Distinct_SUIRAT = n_distinct(COUNTRY)) %>%
arrange(desc(Distinct_SUIRAT)) %>%
slice(1:5)## # A tibble: 5 x 2
## SUIRAT Distinct_SUIRAT
## <dbl> <int>
## 1 10.6 15
## 2 7.6 8
## 3 1.7 7
## 4 2 7
## 5 2.8 7(SUIRAT_Unique_Country <- DPA[round(DPA$SUIRAT,digits=1)==10.6,c("COUNTRY")])## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Congo, Dem. Rep." "Faroe Islands"
## [21] "French Polynesia" "Guam"
## [23] "Hong Kong SAR, China" "Kosovo"
## [25] "Liechtenstein" "Macao SAR, China"
## [27] "New Caledonia" "Puerto Rico"
## [29] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [31] "West Bank and Gaza"(AnomalousVariables_Unique_Country <- MEAIMM_Unique_Country)## [1] "Aruba" "Bermuda"
## [3] "Channel Islands" "Faroe Islands"
## [5] "French Polynesia" "Guam"
## [7] "Hong Kong SAR, China" "Kosovo"
## [9] "Liechtenstein" "Macao SAR, China"
## [11] "New Caledonia" "Puerto Rico"
## [13] "St. Martin (French part)" "Virgin Islands (U.S.)"
## [15] "West Bank and Gaza" "Aruba"
## [17] "Bermuda" "Channel Islands"
## [19] "Faroe Islands" "French Polynesia"
## [21] "Guam" "Hong Kong SAR, China"
## [23] "Kosovo" "Liechtenstein"
## [25] "Macao SAR, China" "New Caledonia"
## [27] "Puerto Rico" "St. Martin (French part)"
## [29] "Virgin Islands (U.S.)" "West Bank and Gaza"##################################
# Removing rows with anomalous values
##################################
dim(DPA)## [1] 394 23DPA <- DPA[!(DPA$COUNTRY %in% AnomalousVariables_Unique_Country),]
dim(DPA)## [1] 364 23##################################
# Listing all Predictors
# for the updated data
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("COUNTRY","YEAR","LIFEXP")]
##################################
# Listing all numeric predictors
# for the updated data
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]1.3.3.2 Zero and Near-Zero Variance
[A] No low variance observed for any predictor using a preprocessing summary from the caret package. The nearZeroVar method using both the freqCut and uniqueCut criteria set at 95/5 and 10, respectively, were applied on the dataset.
Code Chunk | Output
##################################
# Zero and Near-Zero Variance
##################################
##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
freqCut = 80/20,
uniqueCut = 10,
saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])## freqRatio percentUnique zeroVar nzv
## YEAR 0 0.2747253 TRUE TRUEif ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
print("No low variance predictors noted.")
} else {
print(paste0("Low variance observed for ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."))
DPA_LowVarianceForRemoval <- (nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))
print(paste0("Low variance can be resolved by removing ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s)."))
for (j in 1:DPA_LowVarianceForRemoval) {
DPA_LowVarianceRemovedVariable <- rownames(DPA_LowVariance[DPA_LowVariance$nzv,])[j]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LowVarianceRemovedVariable))
}
DPA %>%
skim() %>%
dplyr::filter(skim_variable %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,]))
}## [1] "Low variance observed for 1 numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."
## [1] "Low variance can be resolved by removing 1 numeric variable(s)."
## [1] "Variable 1 for removal: YEAR"| Name | Piped data |
| Number of rows | 364 |
| Number of columns | 23 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| YEAR | 0 | 1 | FALSE | 1 | 201: 364 |
1.3.3.3 Collinearity
[A] High correlation were noted for 7 pairs of numeric predictors with Pearson correlation coefficients >80% as confirmed using the preprocessing summaries from the caret package.
[A.1] URBPOP and RURPOP = -100.00%
[A.2] GDP and GNI = +99.99%
[A.3] DPTIMM and HEPIMM = +95.03%
[A.4] DPTIMM and MEAIMM = +88.17%
[A.5] HEPIMM and MEAIMM = +86.42%
[A.6] CLTECH and SANSER = +86.21%
[A.7] INFMOR and SANSER = -82.41%
Code Chunk | Output
##################################
# Collinearity
##################################
##################################
# Visualizing pairwise correlation between predictors
##################################
(DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"))## UNEMPR INFMOR GDP GNI CLTECH PERCAP
## UNEMPR 1.00000000 0.08921802 -0.09715921 -0.09686799 0.06069602 -0.18518395
## INFMOR 0.08921802 1.00000000 -0.16691246 -0.16605106 -0.77581569 -0.51823250
## GDP -0.09715921 -0.16691246 1.00000000 0.99990577 0.13752124 0.26220470
## GNI -0.09686799 -0.16605106 0.99990577 1.00000000 0.13685071 0.26229594
## CLTECH 0.06069602 -0.77581569 0.13752124 0.13685071 1.00000000 0.52998364
## PERCAP -0.18518395 -0.51823250 0.26220470 0.26229594 0.52998364 1.00000000
## RTIMOR 0.13453252 0.65132565 -0.11475473 -0.11451028 -0.59183716 -0.56088001
## TUBINC 0.16124757 0.58376872 -0.09323916 -0.09283727 -0.55243739 -0.38062151
## DPTIMM -0.12876443 -0.58547646 0.10789977 0.10739430 0.45449772 0.31093085
## HEPIMM -0.09314054 -0.51310504 0.08470102 0.08441932 0.38820759 0.19114586
## MEAIMM -0.15097736 -0.58103197 0.10108399 0.10027512 0.50455989 0.31282969
## HOSBED -0.09030568 -0.52521834 0.13340127 0.13526591 0.43901930 0.32835573
## SANSER 0.01743227 -0.82413990 0.15602727 0.15526943 0.86208147 0.48509427
## TUBTRT -0.05294186 0.30145355 -0.02991535 -0.02993892 -0.32585084 -0.38908786
## URBPOP 0.08774280 -0.53383928 0.16951798 0.16915063 0.64092796 0.57384548
## RURPOP -0.08774280 0.53383928 -0.16951798 -0.16915063 -0.64092796 -0.57384548
## NCOMOR 0.08876876 0.47793168 -0.15463824 -0.15418319 -0.47327879 -0.51434429
## SUIRAT 0.04227853 0.01383619 0.05563045 0.05591448 0.05255453 0.08998525
## RTIMOR TUBINC DPTIMM HEPIMM MEAIMM HOSBED
## UNEMPR 0.13453252 0.16124757 -0.1287644 -0.09314054 -0.15097736 -0.09030568
## INFMOR 0.65132565 0.58376872 -0.5854765 -0.51310504 -0.58103197 -0.52521834
## GDP -0.11475473 -0.09323916 0.1078998 0.08470102 0.10108399 0.13340127
## GNI -0.11451028 -0.09283727 0.1073943 0.08441932 0.10027512 0.13526591
## CLTECH -0.59183716 -0.55243739 0.4544977 0.38820759 0.50455989 0.43901930
## PERCAP -0.56088001 -0.38062151 0.3109309 0.19114586 0.31282969 0.32835573
## RTIMOR 1.00000000 0.41804642 -0.3375163 -0.26020225 -0.30876424 -0.49070005
## TUBINC 0.41804642 1.00000000 -0.3789808 -0.32542031 -0.38118795 -0.19831333
## DPTIMM -0.33751628 -0.37898079 1.0000000 0.95025399 0.88174261 0.32804245
## HEPIMM -0.26020225 -0.32542031 0.9502540 1.00000000 0.86420155 0.27828765
## MEAIMM -0.30876424 -0.38118795 0.8817426 0.86420155 1.00000000 0.33921761
## HOSBED -0.49070005 -0.19831333 0.3280425 0.27828765 0.33921761 1.00000000
## SANSER -0.65108358 -0.55657212 0.4749098 0.41097807 0.52634353 0.48298288
## TUBTRT 0.32106450 0.23187021 -0.1508346 -0.10718796 -0.15128872 -0.20100265
## URBPOP -0.39930232 -0.33604248 0.2477115 0.16791625 0.27392265 0.29499246
## RURPOP 0.39930232 0.33604248 -0.2477115 -0.16791625 -0.27392265 -0.29499246
## NCOMOR 0.27623132 0.44177318 -0.2463052 -0.18345234 -0.25685607 -0.14297975
## SUIRAT -0.08928389 0.13461778 0.1016538 0.07309085 0.07173647 0.23601976
## SANSER TUBTRT URBPOP RURPOP NCOMOR SUIRAT
## UNEMPR 0.01743227 -0.05294186 0.08774280 -0.08774280 0.08876876 0.04227853
## INFMOR -0.82413990 0.30145355 -0.53383928 0.53383928 0.47793168 0.01383619
## GDP 0.15602727 -0.02991535 0.16951798 -0.16951798 -0.15463824 0.05563045
## GNI 0.15526943 -0.02993892 0.16915063 -0.16915063 -0.15418319 0.05591448
## CLTECH 0.86208147 -0.32585084 0.64092796 -0.64092796 -0.47327879 0.05255453
## PERCAP 0.48509427 -0.38908786 0.57384548 -0.57384548 -0.51434429 0.08998525
## RTIMOR -0.65108358 0.32106450 -0.39930232 0.39930232 0.27623132 -0.08928389
## TUBINC -0.55657212 0.23187021 -0.33604248 0.33604248 0.44177318 0.13461778
## DPTIMM 0.47490980 -0.15083460 0.24771148 -0.24771148 -0.24630524 0.10165384
## HEPIMM 0.41097807 -0.10718796 0.16791625 -0.16791625 -0.18345234 0.07309085
## MEAIMM 0.52634353 -0.15128872 0.27392265 -0.27392265 -0.25685607 0.07173647
## HOSBED 0.48298288 -0.20100265 0.29499246 -0.29499246 -0.14297975 0.23601976
## SANSER 1.00000000 -0.29773553 0.57044364 -0.57044364 -0.38904255 0.06984751
## TUBTRT -0.29773553 1.00000000 -0.31075615 0.31075615 0.24910749 -0.03195240
## URBPOP 0.57044364 -0.31075615 1.00000000 -1.00000000 -0.49977042 0.00916115
## RURPOP -0.57044364 0.31075615 -1.00000000 1.00000000 0.49977042 -0.00916115
## NCOMOR -0.38904255 0.24910749 -0.49977042 0.49977042 1.00000000 0.43953077
## SUIRAT 0.06984751 -0.03195240 0.00916115 -0.00916115 0.43953077 1.00000000DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
method = "pearson",
conf.level = 0.95)
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "circle",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "number",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")
##################################
# Identifying the highly correlated variables
##################################
DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs")
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)])>0.80))## [1] 7if (DPA_HighlyCorrelatedCount > 0) {
DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.80)
(DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
print(paste0("High correlation can be resolved by removing ",
(DPA_HighlyCorrelatedForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_HighlyCorrelatedForRemoval) {
DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_HighlyCorrelatedRemovedVariable))
}
}## [1] "High correlation can be resolved by removing 6 numeric variable(s)."
## [1] "Variable 1 for removal: INFMOR"
## [1] "Variable 2 for removal: CLTECH"
## [1] "Variable 3 for removal: URBPOP"
## [1] "Variable 4 for removal: MEAIMM"
## [1] "Variable 5 for removal: DPTIMM"
## [1] "Variable 6 for removal: GNI"1.3.3.4 Linear Dependencies
[A] No linear dependencies noted for any subset of numeric variables using the preprocessing summary from the caret package applying the findLinearCombos method which utilizes the QR decomposition of a matrix to enumerate sets of linear combinations (if they exist).
Code Chunk | Output
##################################
# Linear Dependencies
##################################
##################################
# Finding linear dependencies
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
##################################
# Identifying the linearly dependent variables
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
(DPA_LinearlyDependentCount <- length(DPA_LinearlyDependent$linearCombos))## [1] 0if (DPA_LinearlyDependentCount == 0) {
print("No linearly dependent predictors noted.")
} else {
print(paste0("Linear dependency observed for ",
(DPA_LinearlyDependentCount),
" subset(s) of numeric variable(s)."))
for (i in 1:DPA_LinearlyDependentCount) {
DPA_LinearlyDependentSubset <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$linearCombos[[i]]]
print(paste0("Linear dependent variable(s) for subset ",
i,
" include: ",
DPA_LinearlyDependentSubset))
}
}## [1] "No linearly dependent predictors noted."##################################
# Identifying the linearly dependent variables for removal
##################################
if (DPA_LinearlyDependentCount > 0) {
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
DPA_LinearlyDependentForRemoval <- length(DPA_LinearlyDependent$remove)
print(paste0("Linear dependency can be resolved by removing ",
(DPA_LinearlyDependentForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_LinearlyDependentForRemoval) {
DPA_LinearlyDependentRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$remove[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LinearlyDependentRemovedVariable))
}
}1.3.3.5 Shape Transformation
[A] Shape transformation was applied to improve against skewness and minimize outliers for data distribution stability using the BoxCox method from the caret package which transforms the distributional shape for predictors with strictly positive values.
[B] Skewness measurements were improved for most except for 1 numeric predictor with Skewness>3.
[B.1] SUIRAT = 3.95
[C] Outliers were minimized for most except for 9 numeric predictors which continued to contain outlying points as noted using the IQR criterion.
[C.1] UNEMPR = 5
[C.2] RTIMOR = 2
[C.3] TUBINC = 28
[C.4] DPTIMM = 24
[C.5] HEPIMM = 8
[C.6] MEAIMM = 18
[C.7] TUBTRT = 30
[C.8] NCOMOR = 2
[C.9] SUIRAT = 27
Code Chunk | Output
##################################
# Shape Transformation
##################################
##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)
for (i in 1:ncol(DPA_BoxCoxTransformed)) {
Median <- format(round(median(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Mean <- format(round(mean(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
Skewness <- format(round(skewness(DPA_BoxCoxTransformed[,i],na.rm = TRUE),2), nsmall=2)
print(
ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
geom_histogram(binwidth=1,color="black", fill="white") +
geom_vline(aes(xintercept=mean(DPA_BoxCoxTransformed[,i])),
color="blue", size=1) +
geom_vline(aes(xintercept=median(DPA_BoxCoxTransformed[,i])),
color="red", size=1) +
theme_bw() +
ylab("Count") +
xlab(names(DPA_BoxCoxTransformed)[i]) +
labs(title=names(DPA_BoxCoxTransformed)[i],
subtitle=paste0("Median = ", Median,
", Mean = ", Mean,
", Skewness = ", Skewness)))
}
##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA_BoxCoxTransformed)) {
Outliers <- boxplot.stats(DPA_BoxCoxTransformed[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA_BoxCoxTransformed[,i] %in% c(Outliers))
print(
ggplot(DPA_BoxCoxTransformed, aes(x=DPA_BoxCoxTransformed[,i])) +
geom_boxplot() +
theme_bw() +
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
xlab(names(DPA_BoxCoxTransformed)[i]) +
labs(title=names(DPA_BoxCoxTransformed)[i],
subtitle=paste0(OutlierCount, " Outlier(s) Detected")))
}
DPA_BoxCoxTransformed <- cbind(DPA_BoxCoxTransformed,DPA[,c("COUNTRY",
"YEAR",
"GENDER",
"CONTIN",
"LIFEXP")])1.3.3.6 Pre-Processed Dataset
[A] A total of 9 variables (8 predictors and 1 metadata) were removed prior to data exploration and modelling due to issues identified during data preprocessing.
[A.1] YEAR = Metadata containing only a single value
[A.2] GNI = High correlation with GDP
[A.3] DPTIMM = High correlation with HEPIMM
[A.4] MEAIMM = High correlation with HEPIMM
[A.5] URBPOP = High correlation with RURPOP
[A.6] SANSER = High skewness even after shape transformation
[A.7] TUBINC = High outlier count even after shape transformation
[A.8] TUBTRT = High outlier count even after shape transformation
[A.9] SUIRAT = High outlier count even after shape transformation
[B] A total of 30 observations were removed prior to data exploration and modelling due to issues identified during data preprocessing.
[B.1] 15 countries were identified to be associated with distributional anomalies showing high number of observations reporting the exact same set of values.
[C] The preprocessed tabular dataset was comprised of 364 observations and 14 variables (including 1 metadata, 1 response and 12 predictors).
[C.1] 364 rows (observations)
[C.2] 14 columns (variables)
[A.2.1] 1/14 instance labels = COUNTRY variable (character)
[A.2.2] 1/14 response = LIFEXP variable (numeric)
[A.2.3] 12/14 predictors = 10/12 numeric + 2/12 factor
[A.2.3.1] GENDER (factor)
[A.2.3.2] CONTIN (factor)
[A.2.3.3] UNEMPR (numeric)
[A.2.3.4] INFMOR (numeric)
[A.2.3.5] GDP (numeric)
[A.2.3.6] CLTECH (numeric)
[A.2.3.7] PERCAP (numeric)
[A.2.3.8] RTIMOR (numeric)
[A.2.3.9] HEPIMM (numeric)
[A.2.3.10] HOSBED (numeric)
[A.2.3.11] RURPOP (numeric)
[A.2.3.12] NCOMOR (numeric)
Code Chunk | Output
##################################
# Creating the pre-modelling
# train set
##################################
PMA <- DPA_BoxCoxTransformed[,!names(DPA_BoxCoxTransformed) %in% c("YEAR",
"GNI",
"DPTIMM",
"MEAIMM",
"URBPOP",
"SANSER",
"TUBINC",
"TUBTRT",
"SUIRAT")]
##################################
# Gathering descriptive statistics
##################################
(PMA_Skimmed <- skim(PMA))| Name | PMA |
| Number of rows | 364 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 2 |
| numeric | 11 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| COUNTRY | 0 | 1 | 4 | 30 | 0 | 182 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| GENDER | 0 | 1 | FALSE | 2 | Mal: 182, Fem: 182 |
| CONTIN | 0 | 1 | FALSE | 6 | Afr: 106, Asi: 94, Eur: 78, Nor: 42 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| UNEMPR | 0 | 1 | 2.13 | 1.21 | -2.05 | 1.42 | 2.00 | 2.90 | 5.26 | ▁▂▇▅▂ |
| INFMOR | 0 | 1 | 2.56 | 1.06 | 0.34 | 1.70 | 2.63 | 3.49 | 4.49 | ▅▆▇▇▆ |
| GDP | 0 | 1 | 3.85 | 2.21 | -1.67 | 2.55 | 3.76 | 5.53 | 9.97 | ▂▇▇▆▁ |
| CLTECH | 0 | 1 | 66.08 | 37.77 | 0.00 | 30.30 | 83.95 | 100.00 | 100.00 | ▃▁▁▂▇ |
| PERCAP | 0 | 1 | 1.75 | 1.40 | -1.48 | 0.65 | 1.80 | 2.82 | 4.73 | ▂▆▇▆▃ |
| RTIMOR | 0 | 1 | 16.97 | 10.72 | 0.00 | 8.00 | 15.30 | 25.60 | 64.60 | ▇▆▅▁▁ |
| HEPIMM | 0 | 1 | 3877.07 | 1006.71 | 612.00 | 3304.98 | 4231.50 | 4704.00 | 4900.00 | ▁▁▁▃▇ |
| HOSBED | 0 | 1 | 0.76 | 0.86 | -1.61 | 0.14 | 0.83 | 1.40 | 2.62 | ▁▅▇▇▃ |
| RURPOP | 0 | 1 | 41.73 | 22.68 | 0.00 | 22.62 | 41.64 | 59.76 | 86.75 | ▅▇▇▆▅ |
| NCOMOR | 0 | 1 | 4.67 | 1.08 | 1.87 | 3.93 | 4.72 | 5.38 | 7.96 | ▂▅▇▃▁ |
| LIFEXP | 0 | 1 | 72.50 | 7.77 | 51.20 | 66.93 | 73.53 | 78.54 | 87.45 | ▁▃▆▇▅ |
1.3.4 Data Exploration
Code Chunk | Output
##################################
# Loading dataset
##################################
PME <- PMA
PME.Numeric <- PME[,sapply(PME, is.numeric), drop = FALSE]
##################################
# Listing all Predictors
##################################
PME.Predictors <- PME[,!names(PME) %in% c("COUNTRY","LIFEXP")]
##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Numeric <- PME.Predictors[,sapply(PME.Predictors, is.numeric), drop = FALSE]
ncol(PME.Predictors.Numeric)## [1] 10##################################
# Listing all numeric Predictors
##################################
PME.Predictors.Factor <- PME.Predictors[,sapply(PME.Predictors, is.factor), drop = FALSE]
ncol(PME.Predictors.Factor)## [1] 2##################################
# Formulating the scatter plot
##################################
featurePlot(x = PME.Predictors.Numeric,
y = PME$LIFEXP,
plot = "scatter",
type = c("p", "smooth"),
span = .5,
layout = c(4, 3))
##################################
# Formulating the box plot
##################################
featurePlot(x = PME.Numeric,
y = PME$GENDER,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
layout = c(4, 3))
##################################
# Formulating the box plot
##################################
featurePlot(x = PME.Numeric,
y = PME$CONTIN,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
layout = c(4, 3))
1.3.5 Feature Selection
1.3.5.1 Locally Weighted Scatterplot Smoothing Pseudo-R-Squared (LOWESSPR)
Code Chunk | Output
##################################
# Evaluating model-independent
# feature importance metrics
##################################
##################################
# Obtaining the LOWESSPR pseudo-R-Squared
##################################
FE_LOWESSPR <- filterVarImp(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
y = PME$LIFEXP,
nonpara = TRUE)
##################################
# Formulating the summary table
##################################
FE_LOWESSPR_Summary <- FE_LOWESSPR
FE_LOWESSPR_Summary$Predictor <- rownames(FE_LOWESSPR)
names(FE_LOWESSPR_Summary)[1] <- "LOWESSPR"
FE_LOWESSPR_Summary$Metric <- rep("LOWESSPR",nrow(FE_LOWESSPR))
FE_LOWESSPR_Summary## LOWESSPR Predictor Metric
## UNEMPR 0.03707208 UNEMPR LOWESSPR
## INFMOR 0.82546121 INFMOR LOWESSPR
## GDP 0.25427692 GDP LOWESSPR
## CLTECH 0.58186548 CLTECH LOWESSPR
## PERCAP 0.62170474 PERCAP LOWESSPR
## RTIMOR 0.51428354 RTIMOR LOWESSPR
## HEPIMM 0.18618812 HEPIMM LOWESSPR
## HOSBED 0.34642501 HOSBED LOWESSPR
## RURPOP 0.35023499 RURPOP LOWESSPR
## NCOMOR 0.59655877 NCOMOR LOWESSPR##################################
# Exploring predictor performance
# using LOWESS
##################################
dotplot(Predictor ~ LOWESSPR | Metric,
FE_LOWESSPR_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.3.5.2 Pearson’s Correlation Coefficient (PCC)
Code Chunk | Output
##################################
# Obtaining the Pearson correlation coefficient
##################################
(FE_PCC <- abs(cor(PME.Numeric, method="pearson")[-10,10]))## UNEMPR INFMOR GDP CLTECH PERCAP RTIMOR HEPIMM
## 0.04661692 0.63238609 0.48959856 0.50254656 0.59491536 0.35048089 0.19826820
## HOSBED RURPOP LIFEXP
## 0.24448432 0.53019287 0.73325984##################################
# Formulating the summary table
##################################
FE_PCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
PCC = FE_PCC,
Metric = rep("PCC", length(FE_PCC)))
FE_PCC_Summary## Predictor PCC Metric
## UNEMPR UNEMPR 0.04661692 PCC
## INFMOR INFMOR 0.63238609 PCC
## GDP GDP 0.48959856 PCC
## CLTECH CLTECH 0.50254656 PCC
## PERCAP PERCAP 0.59491536 PCC
## RTIMOR RTIMOR 0.35048089 PCC
## HEPIMM HEPIMM 0.19826820 PCC
## HOSBED HOSBED 0.24448432 PCC
## RURPOP RURPOP 0.53019287 PCC
## LIFEXP NCOMOR 0.73325984 PCC##################################
# Exploring predictor performance
# using PCC
##################################
dotplot(Predictor ~ PCC | Metric,
FE_PCC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.3.5.3 Spearman’s Rank Correlation Coefficient (SRCC)
Code Chunk | Output
##################################
# Obtaining the Spearman's rank correlation coefficient
##################################
(FE_SRCC <- abs(cor(PME.Numeric, method="spearman")[-10,10]))## UNEMPR INFMOR GDP CLTECH PERCAP RTIMOR HEPIMM
## 0.04597079 0.63560349 0.47632718 0.56524903 0.60047700 0.40980009 0.18007328
## HOSBED RURPOP LIFEXP
## 0.26303483 0.54071785 0.78912805##################################
# Formulating the summary table
##################################
FE_SRCC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
SRCC = FE_SRCC,
Metric = rep("SRCC", length(FE_SRCC)))
FE_SRCC_Summary## Predictor SRCC Metric
## UNEMPR UNEMPR 0.04597079 SRCC
## INFMOR INFMOR 0.63560349 SRCC
## GDP GDP 0.47632718 SRCC
## CLTECH CLTECH 0.56524903 SRCC
## PERCAP PERCAP 0.60047700 SRCC
## RTIMOR RTIMOR 0.40980009 SRCC
## HEPIMM HEPIMM 0.18007328 SRCC
## HOSBED HOSBED 0.26303483 SRCC
## RURPOP RURPOP 0.54071785 SRCC
## LIFEXP NCOMOR 0.78912805 SRCC##################################
# Exploring predictor performance
# using SRCC
##################################
dotplot(Predictor ~ SRCC | Metric,
FE_SRCC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.3.5.4 Maximal Information Coefficient (MIC)
Code Chunk | Output
##################################
# Obtaining the maximal information coefficient
##################################
FE_MIC <- mine(x = PME.Numeric[,!names(PME.Numeric) %in% c("LIFEXP")],
y = PME$LIFEXP)$MIC
##################################
# Formulating the summary table
##################################
FE_MIC_Summary <- data.frame(Predictor = names(PME.Numeric)[1:(ncol(PME.Numeric)-1)],
MIC = FE_MIC[,1],
Metric = rep("MIC", length(FE_MIC)))
FE_MIC_Summary## Predictor MIC Metric
## 1 UNEMPR 0.1911950 MIC
## 2 INFMOR 0.7083938 MIC
## 3 GDP 0.3234249 MIC
## 4 CLTECH 0.5099735 MIC
## 5 PERCAP 0.5502257 MIC
## 6 RTIMOR 0.4771528 MIC
## 7 HEPIMM 0.2652364 MIC
## 8 HOSBED 0.3711493 MIC
## 9 RURPOP 0.4097827 MIC
## 10 NCOMOR 0.6438989 MIC##################################
# Exploring predictor performance
# using MIC
##################################
dotplot(Predictor ~ MIC | Metric,
FE_MIC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.3.5.5 Relief Values (RV)
Code Chunk | Output
##################################
# Obtaining the relief values
##################################
FE_RV <- attrEval(LIFEXP ~ .,
data = PME.Numeric,
estimator = "RReliefFequalK")
##################################
# Formulating the summary table
##################################
FE_RV_Summary <- data.frame(Predictor = names(FE_RV),
RV = FE_RV,
Metric = rep("RV", length(FE_RV)))
FE_RV_Summary## Predictor RV Metric
## UNEMPR UNEMPR -0.01810789 RV
## INFMOR INFMOR 0.09365306 RV
## GDP GDP -0.09938056 RV
## CLTECH CLTECH 0.02105440 RV
## PERCAP PERCAP -0.04869230 RV
## RTIMOR RTIMOR 0.01046617 RV
## HEPIMM HEPIMM -0.03530648 RV
## HOSBED HOSBED -0.06597469 RV
## RURPOP RURPOP -0.10732377 RV
## NCOMOR NCOMOR 0.29910591 RV##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ RV | Metric,
FE_RV_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.3.6 Model Development and Performance Estimation
1.3.6.1 Stochastic Gradient Boosting (GBM)
Code Chunk | Output
##################################
# Preparing the dataset for
# model development and test
##################################
set.seed(12345678)
trainIndex <- createDataPartition(PME$LIFEXP,
p = 0.8,
list = FALSE,
times = 1)
##################################
# Formulating the model development data
##################################
MD <- PME[ trainIndex,]
##################################
# Formulating the model test data
##################################
MT <- PME[-trainIndex,]
##################################
# Preparing the dataset for
# model development
##################################
MD <- MD[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]
MD.Model.Predictors <- MD[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR")]
##################################
# Preparing the dataset for
# model test
##################################
MT <- MT[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")]
MT.Model.Predictors <- MT[,c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR")]
##################################
# Creating consistent fold assignments
# for the 10-Fold Cross Validation process
##################################
set.seed(12345678)
KFold_Indices <- createFolds(MD$LIFEXP,
k = 10,
returnTrain=TRUE)
KFold_Control <- trainControl(method="cv",
index=KFold_Indices)
##################################
# Defining the model hyperparameter values
# for the GBM model
##################################
GBM_Grid = expand.grid(n.trees = c(100, 200, 300),
interaction.depth = c(1, 3, 5),
shrinkage = c(0.10,0.05,0.01),
n.minobsinnode = c(15,10,5))
##################################
# Running the GBM model
# by setting the caret method to 'gbm'
##################################
set.seed(12345678)
GBM_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "gbm",
tuneGrid = GBM_Grid,
trControl = KFold_Control)## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0192 nan 0.0100 0.7763
## 2 60.2652 nan 0.0100 0.7757
## 3 59.4737 nan 0.0100 0.7510
## 4 58.7221 nan 0.0100 0.7598
## 5 57.9938 nan 0.0100 0.7048
## 6 57.2211 nan 0.0100 0.7026
## 7 56.5150 nan 0.0100 0.6792
## 8 55.8126 nan 0.0100 0.7336
## 9 55.1307 nan 0.0100 0.6511
## 10 54.4439 nan 0.0100 0.6352
## 20 48.4888 nan 0.0100 0.4724
## 40 38.9299 nan 0.0100 0.4255
## 60 31.8093 nan 0.0100 0.2926
## 80 26.3714 nan 0.0100 0.2027
## 100 22.1975 nan 0.0100 0.1622
## 120 18.9764 nan 0.0100 0.1006
## 140 16.3808 nan 0.0100 0.0852
## 160 14.4027 nan 0.0100 0.0871
## 180 12.7414 nan 0.0100 0.0528
## 200 11.4125 nan 0.0100 0.0519
## 220 10.2835 nan 0.0100 0.0410
## 240 9.3387 nan 0.0100 0.0411
## 260 8.5430 nan 0.0100 0.0392
## 280 7.8728 nan 0.0100 0.0167
## 300 7.2959 nan 0.0100 0.0184
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0182 nan 0.0100 0.8361
## 2 60.2779 nan 0.0100 0.8155
## 3 59.5017 nan 0.0100 0.7081
## 4 58.7437 nan 0.0100 0.6873
## 5 57.9931 nan 0.0100 0.7436
## 6 57.2779 nan 0.0100 0.6835
## 7 56.5913 nan 0.0100 0.7197
## 8 55.9075 nan 0.0100 0.6813
## 9 55.2119 nan 0.0100 0.6398
## 10 54.5418 nan 0.0100 0.6806
## 20 48.5210 nan 0.0100 0.5257
## 40 39.0515 nan 0.0100 0.3873
## 60 31.8911 nan 0.0100 0.2684
## 80 26.6252 nan 0.0100 0.2079
## 100 22.3697 nan 0.0100 0.1577
## 120 19.1586 nan 0.0100 0.1278
## 140 16.6712 nan 0.0100 0.0969
## 160 14.6273 nan 0.0100 0.0773
## 180 12.9450 nan 0.0100 0.0487
## 200 11.5525 nan 0.0100 0.0149
## 220 10.3859 nan 0.0100 0.0366
## 240 9.4442 nan 0.0100 0.0302
## 260 8.6273 nan 0.0100 0.0306
## 280 7.9543 nan 0.0100 0.0267
## 300 7.3607 nan 0.0100 0.0204
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0556 nan 0.0100 0.7589
## 2 60.3011 nan 0.0100 0.7607
## 3 59.5410 nan 0.0100 0.7503
## 4 58.8573 nan 0.0100 0.6862
## 5 58.0926 nan 0.0100 0.7456
## 6 57.3219 nan 0.0100 0.6947
## 7 56.5769 nan 0.0100 0.7003
## 8 55.8379 nan 0.0100 0.6442
## 9 55.1650 nan 0.0100 0.6792
## 10 54.5170 nan 0.0100 0.6986
## 20 48.4557 nan 0.0100 0.5444
## 40 39.1432 nan 0.0100 0.3891
## 60 32.1819 nan 0.0100 0.2893
## 80 26.7656 nan 0.0100 0.2214
## 100 22.5205 nan 0.0100 0.1801
## 120 19.2565 nan 0.0100 0.1308
## 140 16.6481 nan 0.0100 0.1039
## 160 14.5722 nan 0.0100 0.0879
## 180 12.9101 nan 0.0100 0.0668
## 200 11.4789 nan 0.0100 0.0583
## 220 10.3831 nan 0.0100 0.0379
## 240 9.4585 nan 0.0100 0.0361
## 260 8.6801 nan 0.0100 0.0311
## 280 8.0069 nan 0.0100 0.0218
## 300 7.4189 nan 0.0100 0.0258
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7811 nan 0.0100 0.9879
## 2 59.7756 nan 0.0100 1.0245
## 3 58.7235 nan 0.0100 0.9914
## 4 57.7344 nan 0.0100 0.9273
## 5 56.8135 nan 0.0100 0.9757
## 6 55.8527 nan 0.0100 0.8346
## 7 54.9932 nan 0.0100 0.8278
## 8 54.0432 nan 0.0100 0.9012
## 9 53.1906 nan 0.0100 0.8415
## 10 52.3483 nan 0.0100 0.8122
## 20 44.4018 nan 0.0100 0.7897
## 40 32.5229 nan 0.0100 0.4547
## 60 24.4382 nan 0.0100 0.2989
## 80 18.6018 nan 0.0100 0.2411
## 100 14.5876 nan 0.0100 0.1549
## 120 11.6671 nan 0.0100 0.1217
## 140 9.5683 nan 0.0100 0.0590
## 160 7.9564 nan 0.0100 0.0660
## 180 6.7628 nan 0.0100 0.0471
## 200 5.8981 nan 0.0100 0.0302
## 220 5.2091 nan 0.0100 0.0163
## 240 4.7261 nan 0.0100 0.0181
## 260 4.3201 nan 0.0100 0.0122
## 280 4.0142 nan 0.0100 0.0040
## 300 3.7828 nan 0.0100 0.0046
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7363 nan 0.0100 0.9393
## 2 59.7677 nan 0.0100 0.9532
## 3 58.8018 nan 0.0100 0.9275
## 4 57.8559 nan 0.0100 0.9172
## 5 56.8602 nan 0.0100 1.0358
## 6 55.8760 nan 0.0100 0.9584
## 7 54.9599 nan 0.0100 0.9441
## 8 54.0550 nan 0.0100 0.8263
## 9 53.1614 nan 0.0100 0.8152
## 10 52.3250 nan 0.0100 0.9347
## 20 44.3860 nan 0.0100 0.7844
## 40 32.2747 nan 0.0100 0.4327
## 60 24.1659 nan 0.0100 0.3232
## 80 18.3571 nan 0.0100 0.2159
## 100 14.3483 nan 0.0100 0.1701
## 120 11.5081 nan 0.0100 0.1229
## 140 9.4528 nan 0.0100 0.0710
## 160 7.9033 nan 0.0100 0.0642
## 180 6.7763 nan 0.0100 0.0325
## 200 5.9195 nan 0.0100 0.0272
## 220 5.2733 nan 0.0100 0.0241
## 240 4.7617 nan 0.0100 0.0130
## 260 4.4171 nan 0.0100 0.0155
## 280 4.1279 nan 0.0100 0.0100
## 300 3.8959 nan 0.0100 0.0051
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7823 nan 0.0100 0.9654
## 2 59.8273 nan 0.0100 0.9559
## 3 58.8419 nan 0.0100 1.0246
## 4 57.8789 nan 0.0100 0.8718
## 5 56.9219 nan 0.0100 0.8905
## 6 55.9560 nan 0.0100 0.9847
## 7 55.0535 nan 0.0100 0.8720
## 8 54.1138 nan 0.0100 0.8692
## 9 53.2551 nan 0.0100 0.9301
## 10 52.4563 nan 0.0100 0.8490
## 20 44.6100 nan 0.0100 0.7685
## 40 32.8376 nan 0.0100 0.4628
## 60 24.6223 nan 0.0100 0.3640
## 80 18.8576 nan 0.0100 0.2061
## 100 14.7512 nan 0.0100 0.1434
## 120 11.8363 nan 0.0100 0.0932
## 140 9.6990 nan 0.0100 0.1010
## 160 8.1543 nan 0.0100 0.0668
## 180 6.9782 nan 0.0100 0.0413
## 200 6.0937 nan 0.0100 0.0303
## 220 5.4293 nan 0.0100 0.0227
## 240 4.9297 nan 0.0100 0.0183
## 260 4.5575 nan 0.0100 0.0107
## 280 4.2786 nan 0.0100 0.0083
## 300 4.0603 nan 0.0100 0.0037
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7301 nan 0.0100 1.0291
## 2 59.6424 nan 0.0100 1.0195
## 3 58.5884 nan 0.0100 0.8919
## 4 57.5610 nan 0.0100 0.8836
## 5 56.5464 nan 0.0100 1.0641
## 6 55.5833 nan 0.0100 0.8985
## 7 54.6208 nan 0.0100 0.8715
## 8 53.6729 nan 0.0100 0.8315
## 9 52.7461 nan 0.0100 0.9069
## 10 51.8164 nan 0.0100 0.8645
## 20 43.5196 nan 0.0100 0.7079
## 40 31.1126 nan 0.0100 0.5218
## 60 22.6816 nan 0.0100 0.3357
## 80 16.9224 nan 0.0100 0.2170
## 100 12.8284 nan 0.0100 0.1488
## 120 10.0438 nan 0.0100 0.1165
## 140 7.9798 nan 0.0100 0.0732
## 160 6.5047 nan 0.0100 0.0622
## 180 5.4528 nan 0.0100 0.0331
## 200 4.7095 nan 0.0100 0.0304
## 220 4.1500 nan 0.0100 0.0162
## 240 3.7521 nan 0.0100 0.0085
## 260 3.4586 nan 0.0100 0.0048
## 280 3.2304 nan 0.0100 0.0034
## 300 3.0364 nan 0.0100 0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7447 nan 0.0100 1.0219
## 2 59.7039 nan 0.0100 1.0660
## 3 58.6502 nan 0.0100 1.0398
## 4 57.6297 nan 0.0100 1.0546
## 5 56.6425 nan 0.0100 1.0440
## 6 55.6402 nan 0.0100 1.0016
## 7 54.6664 nan 0.0100 0.9037
## 8 53.7131 nan 0.0100 0.9369
## 9 52.7940 nan 0.0100 0.9479
## 10 51.8332 nan 0.0100 0.9079
## 20 43.5255 nan 0.0100 0.7096
## 40 31.1589 nan 0.0100 0.4859
## 60 22.7958 nan 0.0100 0.3179
## 80 16.9616 nan 0.0100 0.2104
## 100 12.9139 nan 0.0100 0.1560
## 120 10.0784 nan 0.0100 0.1178
## 140 8.0651 nan 0.0100 0.0728
## 160 6.6455 nan 0.0100 0.0470
## 180 5.6044 nan 0.0100 0.0368
## 200 4.8571 nan 0.0100 0.0313
## 220 4.3103 nan 0.0100 0.0125
## 240 3.9046 nan 0.0100 0.0112
## 260 3.5932 nan 0.0100 0.0096
## 280 3.3656 nan 0.0100 0.0025
## 300 3.1936 nan 0.0100 0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.7644 nan 0.0100 1.0981
## 2 59.6868 nan 0.0100 1.0994
## 3 58.6746 nan 0.0100 1.0847
## 4 57.6006 nan 0.0100 1.1392
## 5 56.6122 nan 0.0100 0.9589
## 6 55.6823 nan 0.0100 1.0254
## 7 54.6742 nan 0.0100 1.0423
## 8 53.7607 nan 0.0100 0.9721
## 9 52.8951 nan 0.0100 0.9395
## 10 52.0213 nan 0.0100 0.9194
## 20 43.8354 nan 0.0100 0.6542
## 40 31.4738 nan 0.0100 0.5421
## 60 23.1368 nan 0.0100 0.3651
## 80 17.2716 nan 0.0100 0.2035
## 100 13.1896 nan 0.0100 0.1619
## 120 10.3864 nan 0.0100 0.1076
## 140 8.4033 nan 0.0100 0.0712
## 160 6.9817 nan 0.0100 0.0588
## 180 6.0054 nan 0.0100 0.0388
## 200 5.2517 nan 0.0100 0.0227
## 220 4.6931 nan 0.0100 0.0165
## 240 4.3071 nan 0.0100 0.0064
## 260 4.0008 nan 0.0100 0.0025
## 280 3.7852 nan 0.0100 0.0075
## 300 3.6251 nan 0.0100 0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0344 nan 0.0500 3.8415
## 2 54.5365 nan 0.0500 3.4273
## 3 51.7720 nan 0.0500 3.1884
## 4 49.0894 nan 0.0500 2.7612
## 5 46.2533 nan 0.0500 2.6837
## 6 43.7354 nan 0.0500 2.4708
## 7 41.2809 nan 0.0500 2.1635
## 8 39.2262 nan 0.0500 2.1419
## 9 37.0853 nan 0.0500 2.2079
## 10 35.1301 nan 0.0500 1.9341
## 20 22.0217 nan 0.0500 0.6677
## 40 11.3559 nan 0.0500 0.2919
## 60 7.2935 nan 0.0500 0.1375
## 80 5.4364 nan 0.0500 0.0672
## 100 4.5723 nan 0.0500 -0.0144
## 120 4.1502 nan 0.0500 0.0075
## 140 3.9273 nan 0.0500 -0.0055
## 160 3.7719 nan 0.0500 -0.0000
## 180 3.6672 nan 0.0500 -0.0064
## 200 3.5875 nan 0.0500 -0.0060
## 220 3.5093 nan 0.0500 -0.0159
## 240 3.4362 nan 0.0500 -0.0065
## 260 3.3752 nan 0.0500 -0.0091
## 280 3.3165 nan 0.0500 -0.0038
## 300 3.2673 nan 0.0500 -0.0031
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.9628 nan 0.0500 3.7315
## 2 54.6727 nan 0.0500 3.4527
## 3 51.5215 nan 0.0500 3.1737
## 4 48.4679 nan 0.0500 2.9660
## 5 45.6125 nan 0.0500 2.6173
## 6 43.1555 nan 0.0500 2.3758
## 7 40.6447 nan 0.0500 2.3076
## 8 38.6491 nan 0.0500 1.7343
## 9 36.8462 nan 0.0500 1.6289
## 10 35.3466 nan 0.0500 1.4689
## 20 22.3990 nan 0.0500 0.4800
## 40 11.4911 nan 0.0500 0.2501
## 60 7.4326 nan 0.0500 0.1619
## 80 5.5370 nan 0.0500 0.0593
## 100 4.6345 nan 0.0500 0.0070
## 120 4.1969 nan 0.0500 0.0073
## 140 3.9968 nan 0.0500 0.0072
## 160 3.8581 nan 0.0500 -0.0145
## 180 3.7538 nan 0.0500 -0.0117
## 200 3.6598 nan 0.0500 -0.0090
## 220 3.5888 nan 0.0500 -0.0061
## 240 3.5186 nan 0.0500 -0.0077
## 260 3.4551 nan 0.0500 -0.0162
## 280 3.4013 nan 0.0500 -0.0084
## 300 3.3618 nan 0.0500 -0.0169
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.6228 nan 0.0500 3.9987
## 2 54.1330 nan 0.0500 3.3888
## 3 51.0764 nan 0.0500 2.9593
## 4 48.2039 nan 0.0500 2.5425
## 5 45.4965 nan 0.0500 2.7583
## 6 42.7987 nan 0.0500 2.4621
## 7 40.5409 nan 0.0500 2.2391
## 8 38.5091 nan 0.0500 2.0108
## 9 36.5919 nan 0.0500 2.1784
## 10 34.6881 nan 0.0500 1.8360
## 20 21.8050 nan 0.0500 0.8761
## 40 11.3694 nan 0.0500 0.2106
## 60 7.4091 nan 0.0500 0.0972
## 80 5.6757 nan 0.0500 0.0369
## 100 4.7825 nan 0.0500 0.0123
## 120 4.3704 nan 0.0500 -0.0012
## 140 4.1583 nan 0.0500 -0.0018
## 160 4.0575 nan 0.0500 -0.0117
## 180 3.9493 nan 0.0500 -0.0036
## 200 3.8721 nan 0.0500 -0.0035
## 220 3.7928 nan 0.0500 -0.0082
## 240 3.7154 nan 0.0500 -0.0074
## 260 3.6505 nan 0.0500 -0.0039
## 280 3.5909 nan 0.0500 -0.0066
## 300 3.5425 nan 0.0500 -0.0068
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5954 nan 0.0500 5.0099
## 2 51.9922 nan 0.0500 4.5935
## 3 47.6779 nan 0.0500 3.7401
## 4 43.9259 nan 0.0500 4.2016
## 5 40.4580 nan 0.0500 3.1794
## 6 37.3158 nan 0.0500 2.5898
## 7 34.4163 nan 0.0500 2.8136
## 8 31.9373 nan 0.0500 2.3894
## 9 29.6482 nan 0.0500 2.1312
## 10 27.6422 nan 0.0500 2.1355
## 20 14.0346 nan 0.0500 0.6351
## 40 5.8839 nan 0.0500 0.1299
## 60 3.7923 nan 0.0500 0.0253
## 80 3.1408 nan 0.0500 -0.0048
## 100 2.8130 nan 0.0500 -0.0304
## 120 2.5901 nan 0.0500 -0.0085
## 140 2.3987 nan 0.0500 -0.0198
## 160 2.2314 nan 0.0500 -0.0156
## 180 2.0850 nan 0.0500 -0.0075
## 200 1.9742 nan 0.0500 -0.0069
## 220 1.8957 nan 0.0500 -0.0090
## 240 1.8090 nan 0.0500 -0.0129
## 260 1.7282 nan 0.0500 -0.0128
## 280 1.6537 nan 0.0500 -0.0055
## 300 1.5882 nan 0.0500 -0.0053
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7704 nan 0.0500 5.1293
## 2 52.0636 nan 0.0500 4.1257
## 3 47.9781 nan 0.0500 3.9736
## 4 44.2478 nan 0.0500 3.7691
## 5 41.0279 nan 0.0500 3.4203
## 6 37.9647 nan 0.0500 2.9650
## 7 35.0765 nan 0.0500 2.9375
## 8 32.3973 nan 0.0500 2.5871
## 9 30.1281 nan 0.0500 2.3746
## 10 28.0278 nan 0.0500 2.2176
## 20 14.2885 nan 0.0500 0.8064
## 40 5.8812 nan 0.0500 0.1339
## 60 3.9463 nan 0.0500 0.0309
## 80 3.3117 nan 0.0500 -0.0012
## 100 3.0017 nan 0.0500 0.0001
## 120 2.8043 nan 0.0500 -0.0145
## 140 2.6628 nan 0.0500 -0.0136
## 160 2.5457 nan 0.0500 -0.0163
## 180 2.4306 nan 0.0500 -0.0114
## 200 2.3195 nan 0.0500 -0.0038
## 220 2.2270 nan 0.0500 -0.0206
## 240 2.1431 nan 0.0500 -0.0173
## 260 2.0566 nan 0.0500 -0.0136
## 280 1.9931 nan 0.0500 -0.0136
## 300 1.9436 nan 0.0500 -0.0095
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5645 nan 0.0500 5.1224
## 2 51.9284 nan 0.0500 3.8676
## 3 48.0714 nan 0.0500 3.8581
## 4 44.3610 nan 0.0500 3.8282
## 5 40.8656 nan 0.0500 3.4710
## 6 37.6051 nan 0.0500 3.2033
## 7 34.8928 nan 0.0500 2.7928
## 8 32.3792 nan 0.0500 2.3185
## 9 30.1353 nan 0.0500 2.5385
## 10 28.0992 nan 0.0500 1.9273
## 20 14.5747 nan 0.0500 0.7630
## 40 5.9520 nan 0.0500 0.1248
## 60 4.1205 nan 0.0500 0.0301
## 80 3.5689 nan 0.0500 -0.0043
## 100 3.2480 nan 0.0500 -0.0127
## 120 3.0308 nan 0.0500 -0.0122
## 140 2.8719 nan 0.0500 -0.0176
## 160 2.7390 nan 0.0500 -0.0111
## 180 2.6283 nan 0.0500 -0.0092
## 200 2.5264 nan 0.0500 -0.0211
## 220 2.4331 nan 0.0500 -0.0157
## 240 2.3688 nan 0.0500 -0.0182
## 260 2.2677 nan 0.0500 -0.0081
## 280 2.2001 nan 0.0500 -0.0168
## 300 2.1255 nan 0.0500 -0.0103
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5588 nan 0.0500 4.7218
## 2 51.5893 nan 0.0500 4.9800
## 3 47.1904 nan 0.0500 4.5704
## 4 43.1887 nan 0.0500 3.4471
## 5 39.5545 nan 0.0500 3.8226
## 6 36.4772 nan 0.0500 3.1387
## 7 33.6011 nan 0.0500 2.6734
## 8 30.9170 nan 0.0500 2.4560
## 9 28.6029 nan 0.0500 2.2412
## 10 26.5061 nan 0.0500 2.0475
## 20 12.6805 nan 0.0500 0.8432
## 40 4.6380 nan 0.0500 0.0789
## 60 3.0019 nan 0.0500 0.0182
## 80 2.4495 nan 0.0500 -0.0166
## 100 2.1448 nan 0.0500 -0.0199
## 120 1.9072 nan 0.0500 -0.0127
## 140 1.7133 nan 0.0500 -0.0077
## 160 1.5620 nan 0.0500 -0.0124
## 180 1.4178 nan 0.0500 -0.0161
## 200 1.3135 nan 0.0500 -0.0071
## 220 1.2133 nan 0.0500 -0.0081
## 240 1.1287 nan 0.0500 -0.0124
## 260 1.0543 nan 0.0500 -0.0106
## 280 0.9757 nan 0.0500 -0.0066
## 300 0.9178 nan 0.0500 -0.0085
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5683 nan 0.0500 4.9344
## 2 51.6944 nan 0.0500 4.8168
## 3 47.2959 nan 0.0500 4.3846
## 4 43.5104 nan 0.0500 3.7302
## 5 39.7695 nan 0.0500 3.7444
## 6 36.6010 nan 0.0500 3.1937
## 7 33.6638 nan 0.0500 2.6948
## 8 30.9358 nan 0.0500 2.6595
## 9 28.4562 nan 0.0500 2.1274
## 10 26.2043 nan 0.0500 2.0773
## 20 12.7507 nan 0.0500 0.6586
## 40 4.8954 nan 0.0500 0.1259
## 60 3.2180 nan 0.0500 -0.0104
## 80 2.7147 nan 0.0500 -0.0113
## 100 2.4543 nan 0.0500 -0.0134
## 120 2.2894 nan 0.0500 -0.0185
## 140 2.1054 nan 0.0500 -0.0231
## 160 1.9550 nan 0.0500 -0.0053
## 180 1.8195 nan 0.0500 -0.0263
## 200 1.6884 nan 0.0500 -0.0134
## 220 1.5804 nan 0.0500 -0.0132
## 240 1.4846 nan 0.0500 -0.0204
## 260 1.4026 nan 0.0500 -0.0103
## 280 1.3298 nan 0.0500 -0.0055
## 300 1.2638 nan 0.0500 -0.0054
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.2670 nan 0.0500 4.9296
## 2 51.4201 nan 0.0500 4.4277
## 3 47.1252 nan 0.0500 4.1616
## 4 43.0992 nan 0.0500 4.4701
## 5 39.5475 nan 0.0500 3.2675
## 6 36.3316 nan 0.0500 2.9151
## 7 33.4274 nan 0.0500 2.7498
## 8 30.8849 nan 0.0500 2.4451
## 9 28.5181 nan 0.0500 2.3685
## 10 26.2967 nan 0.0500 2.2567
## 20 12.7991 nan 0.0500 0.7365
## 40 5.1023 nan 0.0500 0.1075
## 60 3.5319 nan 0.0500 0.0196
## 80 3.0871 nan 0.0500 -0.0160
## 100 2.8119 nan 0.0500 -0.0116
## 120 2.6216 nan 0.0500 -0.0075
## 140 2.4403 nan 0.0500 -0.0120
## 160 2.3001 nan 0.0500 -0.0060
## 180 2.1775 nan 0.0500 -0.0142
## 200 2.0699 nan 0.0500 -0.0169
## 220 1.9702 nan 0.0500 -0.0133
## 240 1.8870 nan 0.0500 -0.0101
## 260 1.7939 nan 0.0500 -0.0079
## 280 1.7193 nan 0.0500 -0.0083
## 300 1.6625 nan 0.0500 -0.0082
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3161 nan 0.1000 7.5967
## 2 48.5321 nan 0.1000 5.9673
## 3 43.3385 nan 0.1000 4.9340
## 4 38.8289 nan 0.1000 4.1032
## 5 34.7630 nan 0.1000 3.8697
## 6 31.3677 nan 0.1000 3.5022
## 7 28.3541 nan 0.1000 2.5892
## 8 25.8490 nan 0.1000 2.3528
## 9 23.3261 nan 0.1000 1.9713
## 10 21.4953 nan 0.1000 1.7198
## 20 11.0330 nan 0.1000 0.5097
## 40 5.4589 nan 0.1000 0.1066
## 60 4.1032 nan 0.1000 0.0118
## 80 3.8045 nan 0.1000 0.0068
## 100 3.6322 nan 0.1000 -0.0227
## 120 3.4930 nan 0.1000 -0.0226
## 140 3.3736 nan 0.1000 -0.0234
## 160 3.3006 nan 0.1000 -0.0176
## 180 3.2299 nan 0.1000 -0.0281
## 200 3.1753 nan 0.1000 -0.0066
## 220 3.1126 nan 0.1000 -0.0165
## 240 3.0482 nan 0.1000 -0.0135
## 260 2.9993 nan 0.1000 -0.0278
## 280 2.9428 nan 0.1000 -0.0157
## 300 2.8895 nan 0.1000 -0.0200
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.8221 nan 0.1000 7.5962
## 2 48.1017 nan 0.1000 6.2012
## 3 42.4392 nan 0.1000 5.1381
## 4 38.6368 nan 0.1000 3.6170
## 5 34.6672 nan 0.1000 3.9919
## 6 31.3575 nan 0.1000 2.6233
## 7 28.5477 nan 0.1000 2.7536
## 8 26.2321 nan 0.1000 2.1725
## 9 23.9798 nan 0.1000 2.2318
## 10 22.0617 nan 0.1000 1.8792
## 20 11.0964 nan 0.1000 0.5872
## 40 5.3627 nan 0.1000 0.0595
## 60 4.1855 nan 0.1000 0.0325
## 80 3.8638 nan 0.1000 -0.0056
## 100 3.6775 nan 0.1000 -0.0361
## 120 3.5706 nan 0.1000 -0.0058
## 140 3.4825 nan 0.1000 -0.0022
## 160 3.4043 nan 0.1000 -0.0162
## 180 3.3384 nan 0.1000 -0.0236
## 200 3.2481 nan 0.1000 -0.0083
## 220 3.1899 nan 0.1000 -0.0293
## 240 3.1343 nan 0.1000 -0.0229
## 260 3.0677 nan 0.1000 -0.0037
## 280 3.0078 nan 0.1000 -0.0093
## 300 2.9503 nan 0.1000 -0.0101
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.6917 nan 0.1000 7.4753
## 2 47.9111 nan 0.1000 6.1290
## 3 42.9440 nan 0.1000 4.9682
## 4 38.6625 nan 0.1000 4.4414
## 5 34.9776 nan 0.1000 3.7037
## 6 31.3760 nan 0.1000 3.4863
## 7 28.5369 nan 0.1000 3.1025
## 8 26.3977 nan 0.1000 1.7627
## 9 23.8295 nan 0.1000 2.4272
## 10 21.8973 nan 0.1000 1.9409
## 20 10.9792 nan 0.1000 0.4816
## 40 5.5861 nan 0.1000 0.0929
## 60 4.4016 nan 0.1000 0.0188
## 80 4.0705 nan 0.1000 -0.0102
## 100 3.9154 nan 0.1000 -0.0088
## 120 3.7406 nan 0.1000 -0.0130
## 140 3.6276 nan 0.1000 -0.0231
## 160 3.5139 nan 0.1000 -0.0102
## 180 3.4294 nan 0.1000 -0.0058
## 200 3.3450 nan 0.1000 -0.0184
## 220 3.2711 nan 0.1000 -0.0101
## 240 3.2038 nan 0.1000 -0.0085
## 260 3.1537 nan 0.1000 -0.0276
## 280 3.0923 nan 0.1000 -0.0144
## 300 3.0416 nan 0.1000 -0.0053
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4726 nan 0.1000 8.9648
## 2 43.8795 nan 0.1000 7.7489
## 3 37.5948 nan 0.1000 6.4576
## 4 32.3294 nan 0.1000 4.7621
## 5 27.5584 nan 0.1000 4.6246
## 6 23.9344 nan 0.1000 3.6162
## 7 20.8036 nan 0.1000 2.9801
## 8 18.0210 nan 0.1000 2.4732
## 9 15.6881 nan 0.1000 2.0917
## 10 14.1115 nan 0.1000 1.4837
## 20 5.7824 nan 0.1000 0.2826
## 40 3.1788 nan 0.1000 -0.0073
## 60 2.6056 nan 0.1000 0.0038
## 80 2.2704 nan 0.1000 -0.0222
## 100 2.0300 nan 0.1000 -0.0106
## 120 1.7811 nan 0.1000 -0.0285
## 140 1.6086 nan 0.1000 -0.0210
## 160 1.4618 nan 0.1000 -0.0226
## 180 1.3599 nan 0.1000 -0.0113
## 200 1.2608 nan 0.1000 -0.0064
## 220 1.1921 nan 0.1000 -0.0145
## 240 1.1012 nan 0.1000 -0.0119
## 260 1.0217 nan 0.1000 -0.0081
## 280 0.9546 nan 0.1000 -0.0104
## 300 0.8919 nan 0.1000 -0.0093
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.2399 nan 0.1000 10.0455
## 2 43.9704 nan 0.1000 7.9486
## 3 38.2288 nan 0.1000 5.2999
## 4 32.3777 nan 0.1000 5.3854
## 5 27.5554 nan 0.1000 4.6569
## 6 23.9197 nan 0.1000 3.8108
## 7 20.9706 nan 0.1000 2.7298
## 8 18.4633 nan 0.1000 2.4062
## 9 15.9500 nan 0.1000 2.1532
## 10 14.1225 nan 0.1000 1.6851
## 20 5.7424 nan 0.1000 0.4124
## 40 3.2993 nan 0.1000 0.0136
## 60 2.7642 nan 0.1000 -0.0247
## 80 2.4472 nan 0.1000 -0.0236
## 100 2.2731 nan 0.1000 -0.0373
## 120 2.1058 nan 0.1000 -0.0137
## 140 1.9387 nan 0.1000 -0.0344
## 160 1.8227 nan 0.1000 -0.0310
## 180 1.6896 nan 0.1000 -0.0236
## 200 1.5886 nan 0.1000 -0.0240
## 220 1.5046 nan 0.1000 -0.0166
## 240 1.4342 nan 0.1000 -0.0292
## 260 1.3655 nan 0.1000 -0.0166
## 280 1.3094 nan 0.1000 -0.0106
## 300 1.2528 nan 0.1000 -0.0109
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.1985 nan 0.1000 8.9546
## 2 44.2912 nan 0.1000 7.8004
## 3 37.7013 nan 0.1000 6.5905
## 4 32.1139 nan 0.1000 4.7618
## 5 27.5704 nan 0.1000 3.8246
## 6 23.8799 nan 0.1000 3.6257
## 7 20.7950 nan 0.1000 3.0542
## 8 18.0271 nan 0.1000 2.5835
## 9 15.8764 nan 0.1000 1.9822
## 10 14.1450 nan 0.1000 1.6344
## 20 6.0919 nan 0.1000 0.2129
## 40 3.5881 nan 0.1000 -0.0376
## 60 3.1061 nan 0.1000 -0.0077
## 80 2.7711 nan 0.1000 -0.0325
## 100 2.5421 nan 0.1000 -0.0315
## 120 2.3426 nan 0.1000 -0.0529
## 140 2.1926 nan 0.1000 -0.0435
## 160 2.0785 nan 0.1000 -0.0148
## 180 1.9620 nan 0.1000 -0.0220
## 200 1.8454 nan 0.1000 -0.0152
## 220 1.7456 nan 0.1000 -0.0089
## 240 1.6746 nan 0.1000 -0.0196
## 260 1.6048 nan 0.1000 -0.0261
## 280 1.5287 nan 0.1000 -0.0253
## 300 1.4665 nan 0.1000 -0.0095
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7145 nan 0.1000 10.3756
## 2 43.0919 nan 0.1000 8.2583
## 3 36.2916 nan 0.1000 6.1223
## 4 30.4362 nan 0.1000 5.4657
## 5 26.1032 nan 0.1000 5.0175
## 6 22.2192 nan 0.1000 4.0364
## 7 19.0044 nan 0.1000 2.7087
## 8 16.5323 nan 0.1000 2.2295
## 9 14.4749 nan 0.1000 1.7116
## 10 12.6749 nan 0.1000 1.8570
## 20 4.8160 nan 0.1000 0.2611
## 40 2.5902 nan 0.1000 -0.0003
## 60 2.0046 nan 0.1000 -0.0276
## 80 1.6307 nan 0.1000 -0.0076
## 100 1.4037 nan 0.1000 -0.0251
## 120 1.2264 nan 0.1000 -0.0189
## 140 1.0591 nan 0.1000 -0.0151
## 160 0.9383 nan 0.1000 -0.0214
## 180 0.8296 nan 0.1000 -0.0079
## 200 0.7226 nan 0.1000 -0.0169
## 220 0.6533 nan 0.1000 -0.0094
## 240 0.5826 nan 0.1000 -0.0124
## 260 0.5241 nan 0.1000 -0.0110
## 280 0.4698 nan 0.1000 -0.0047
## 300 0.4292 nan 0.1000 -0.0101
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.6871 nan 0.1000 9.4432
## 2 43.1760 nan 0.1000 8.5284
## 3 36.2803 nan 0.1000 7.2351
## 4 30.6930 nan 0.1000 5.7188
## 5 26.0779 nan 0.1000 4.5128
## 6 22.0238 nan 0.1000 3.9601
## 7 19.0725 nan 0.1000 2.9886
## 8 16.4103 nan 0.1000 2.5538
## 9 14.3840 nan 0.1000 1.8303
## 10 12.4843 nan 0.1000 1.7952
## 20 4.8169 nan 0.1000 0.2098
## 40 2.7760 nan 0.1000 0.0002
## 60 2.2630 nan 0.1000 -0.0166
## 80 1.9706 nan 0.1000 -0.0431
## 100 1.7534 nan 0.1000 -0.0199
## 120 1.5730 nan 0.1000 -0.0319
## 140 1.4006 nan 0.1000 -0.0157
## 160 1.2674 nan 0.1000 -0.0187
## 180 1.1430 nan 0.1000 -0.0192
## 200 1.0472 nan 0.1000 -0.0167
## 220 0.9740 nan 0.1000 -0.0158
## 240 0.8996 nan 0.1000 -0.0077
## 260 0.8109 nan 0.1000 -0.0153
## 280 0.7482 nan 0.1000 -0.0095
## 300 0.7035 nan 0.1000 -0.0123
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5345 nan 0.1000 10.3054
## 2 43.0925 nan 0.1000 8.4375
## 3 36.0068 nan 0.1000 7.2820
## 4 30.4364 nan 0.1000 5.7147
## 5 25.4884 nan 0.1000 4.8591
## 6 21.7094 nan 0.1000 3.4260
## 7 18.6532 nan 0.1000 3.0285
## 8 16.0543 nan 0.1000 2.5504
## 9 14.1676 nan 0.1000 1.7930
## 10 12.3412 nan 0.1000 1.6605
## 20 5.0075 nan 0.1000 0.2380
## 40 3.1119 nan 0.1000 -0.0072
## 60 2.6684 nan 0.1000 -0.0126
## 80 2.4027 nan 0.1000 -0.0623
## 100 2.1318 nan 0.1000 -0.0220
## 120 1.9685 nan 0.1000 -0.0340
## 140 1.8308 nan 0.1000 -0.0214
## 160 1.6708 nan 0.1000 -0.0243
## 180 1.5475 nan 0.1000 -0.0271
## 200 1.4144 nan 0.1000 -0.0108
## 220 1.3058 nan 0.1000 -0.0215
## 240 1.2154 nan 0.1000 -0.0254
## 260 1.1360 nan 0.1000 -0.0090
## 280 1.0795 nan 0.1000 -0.0114
## 300 1.0089 nan 0.1000 -0.0138
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2146 nan 0.0100 0.7571
## 2 60.4418 nan 0.0100 0.7667
## 3 59.6427 nan 0.0100 0.7732
## 4 58.8563 nan 0.0100 0.7111
## 5 58.1218 nan 0.0100 0.7164
## 6 57.3844 nan 0.0100 0.7175
## 7 56.6627 nan 0.0100 0.7603
## 8 55.9861 nan 0.0100 0.6582
## 9 55.3267 nan 0.0100 0.6970
## 10 54.6633 nan 0.0100 0.6140
## 20 48.7058 nan 0.0100 0.4878
## 40 39.0761 nan 0.0100 0.3942
## 60 31.8254 nan 0.0100 0.3262
## 80 26.2326 nan 0.0100 0.2038
## 100 22.0595 nan 0.0100 0.1801
## 120 18.8423 nan 0.0100 0.0819
## 140 16.3048 nan 0.0100 0.0959
## 160 14.1778 nan 0.0100 0.0775
## 180 12.5247 nan 0.0100 0.0552
## 200 11.1925 nan 0.0100 0.0533
## 220 10.0811 nan 0.0100 0.0429
## 240 9.1473 nan 0.0100 0.0298
## 260 8.3516 nan 0.0100 0.0244
## 280 7.6500 nan 0.0100 0.0266
## 300 7.0593 nan 0.0100 0.0240
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2101 nan 0.0100 0.7058
## 2 60.4066 nan 0.0100 0.7338
## 3 59.6159 nan 0.0100 0.7538
## 4 58.9031 nan 0.0100 0.7667
## 5 58.2160 nan 0.0100 0.6699
## 6 57.4825 nan 0.0100 0.7249
## 7 56.7370 nan 0.0100 0.7177
## 8 56.0350 nan 0.0100 0.6768
## 9 55.3693 nan 0.0100 0.6461
## 10 54.7009 nan 0.0100 0.6582
## 20 48.5406 nan 0.0100 0.5734
## 40 39.1154 nan 0.0100 0.4282
## 60 31.9170 nan 0.0100 0.3021
## 80 26.4411 nan 0.0100 0.2322
## 100 22.1562 nan 0.0100 0.1720
## 120 18.9394 nan 0.0100 0.1307
## 140 16.2945 nan 0.0100 0.0892
## 160 14.2501 nan 0.0100 0.0950
## 180 12.6227 nan 0.0100 0.0704
## 200 11.2290 nan 0.0100 0.0646
## 220 10.1104 nan 0.0100 0.0350
## 240 9.1508 nan 0.0100 0.0344
## 260 8.3164 nan 0.0100 0.0368
## 280 7.6501 nan 0.0100 0.0299
## 300 7.0750 nan 0.0100 0.0142
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2331 nan 0.0100 0.7881
## 2 60.4461 nan 0.0100 0.7510
## 3 59.6411 nan 0.0100 0.6633
## 4 58.8597 nan 0.0100 0.7500
## 5 58.1200 nan 0.0100 0.7374
## 6 57.4290 nan 0.0100 0.6743
## 7 56.7738 nan 0.0100 0.6543
## 8 56.1146 nan 0.0100 0.6179
## 9 55.4434 nan 0.0100 0.6502
## 10 54.7779 nan 0.0100 0.6477
## 20 48.7296 nan 0.0100 0.5429
## 40 39.1938 nan 0.0100 0.3492
## 60 31.7242 nan 0.0100 0.2906
## 80 26.2641 nan 0.0100 0.1913
## 100 22.0691 nan 0.0100 0.1638
## 120 18.7761 nan 0.0100 0.1298
## 140 16.2797 nan 0.0100 0.0982
## 160 14.2441 nan 0.0100 0.0761
## 180 12.5985 nan 0.0100 0.0769
## 200 11.2723 nan 0.0100 0.0487
## 220 10.1543 nan 0.0100 0.0372
## 240 9.1683 nan 0.0100 0.0372
## 260 8.3710 nan 0.0100 0.0340
## 280 7.7122 nan 0.0100 0.0182
## 300 7.1286 nan 0.0100 0.0171
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9207 nan 0.0100 1.0617
## 2 59.9500 nan 0.0100 0.9774
## 3 58.9181 nan 0.0100 1.0423
## 4 57.9789 nan 0.0100 0.9182
## 5 57.0590 nan 0.0100 1.0044
## 6 56.1512 nan 0.0100 0.9246
## 7 55.1854 nan 0.0100 0.9386
## 8 54.2744 nan 0.0100 0.9058
## 9 53.4214 nan 0.0100 0.8462
## 10 52.5447 nan 0.0100 0.8344
## 20 44.5667 nan 0.0100 0.6811
## 40 32.5573 nan 0.0100 0.5254
## 60 24.2591 nan 0.0100 0.3446
## 80 18.4772 nan 0.0100 0.2190
## 100 14.3530 nan 0.0100 0.1786
## 120 11.3916 nan 0.0100 0.1027
## 140 9.3115 nan 0.0100 0.0759
## 160 7.7075 nan 0.0100 0.0542
## 180 6.5679 nan 0.0100 0.0291
## 200 5.6977 nan 0.0100 0.0229
## 220 5.0259 nan 0.0100 0.0228
## 240 4.5150 nan 0.0100 0.0219
## 260 4.1228 nan 0.0100 0.0128
## 280 3.8013 nan 0.0100 0.0099
## 300 3.5468 nan 0.0100 0.0063
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9683 nan 0.0100 0.8833
## 2 59.9679 nan 0.0100 0.9525
## 3 58.9316 nan 0.0100 0.9552
## 4 57.9438 nan 0.0100 0.9942
## 5 56.9863 nan 0.0100 0.8982
## 6 56.0058 nan 0.0100 0.9342
## 7 55.1156 nan 0.0100 0.9748
## 8 54.2441 nan 0.0100 0.9168
## 9 53.3840 nan 0.0100 0.8944
## 10 52.5425 nan 0.0100 0.8226
## 20 44.7535 nan 0.0100 0.7515
## 40 32.6659 nan 0.0100 0.4574
## 60 24.3050 nan 0.0100 0.3278
## 80 18.5316 nan 0.0100 0.2395
## 100 14.5070 nan 0.0100 0.1561
## 120 11.5341 nan 0.0100 0.1093
## 140 9.4060 nan 0.0100 0.0801
## 160 7.8312 nan 0.0100 0.0603
## 180 6.6948 nan 0.0100 0.0406
## 200 5.8077 nan 0.0100 0.0251
## 220 5.1159 nan 0.0100 0.0230
## 240 4.5981 nan 0.0100 0.0181
## 260 4.2136 nan 0.0100 0.0110
## 280 3.9053 nan 0.0100 0.0112
## 300 3.6720 nan 0.0100 0.0048
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0170 nan 0.0100 1.0234
## 2 59.9716 nan 0.0100 0.9814
## 3 58.9577 nan 0.0100 1.0491
## 4 57.9841 nan 0.0100 1.0418
## 5 57.0659 nan 0.0100 0.9546
## 6 56.0661 nan 0.0100 0.9480
## 7 55.0854 nan 0.0100 0.9326
## 8 54.1699 nan 0.0100 0.9218
## 9 53.2629 nan 0.0100 1.0079
## 10 52.3588 nan 0.0100 0.8554
## 20 44.5512 nan 0.0100 0.6441
## 40 32.5981 nan 0.0100 0.4712
## 60 24.3222 nan 0.0100 0.3173
## 80 18.6177 nan 0.0100 0.2361
## 100 14.6100 nan 0.0100 0.1648
## 120 11.6843 nan 0.0100 0.1067
## 140 9.5559 nan 0.0100 0.0870
## 160 8.0146 nan 0.0100 0.0602
## 180 6.8107 nan 0.0100 0.0493
## 200 5.9598 nan 0.0100 0.0210
## 220 5.3236 nan 0.0100 0.0226
## 240 4.7931 nan 0.0100 0.0190
## 260 4.4029 nan 0.0100 0.0096
## 280 4.1061 nan 0.0100 0.0102
## 300 3.8722 nan 0.0100 0.0061
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8599 nan 0.0100 1.1045
## 2 59.7604 nan 0.0100 0.9577
## 3 58.7324 nan 0.0100 1.0759
## 4 57.6728 nan 0.0100 1.0633
## 5 56.6852 nan 0.0100 1.0514
## 6 55.6812 nan 0.0100 0.9947
## 7 54.6859 nan 0.0100 1.0101
## 8 53.7895 nan 0.0100 1.0009
## 9 52.9049 nan 0.0100 0.9661
## 10 52.0180 nan 0.0100 0.8686
## 20 43.7365 nan 0.0100 0.6808
## 40 31.1704 nan 0.0100 0.5059
## 60 22.6080 nan 0.0100 0.3243
## 80 16.7924 nan 0.0100 0.2327
## 100 12.6529 nan 0.0100 0.1739
## 120 9.8255 nan 0.0100 0.1081
## 140 7.8145 nan 0.0100 0.0805
## 160 6.3392 nan 0.0100 0.0546
## 180 5.2905 nan 0.0100 0.0444
## 200 4.5580 nan 0.0100 0.0262
## 220 4.0028 nan 0.0100 0.0108
## 240 3.5816 nan 0.0100 0.0081
## 260 3.2739 nan 0.0100 0.0070
## 280 3.0261 nan 0.0100 0.0037
## 300 2.8510 nan 0.0100 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8466 nan 0.0100 1.1105
## 2 59.8173 nan 0.0100 1.0447
## 3 58.7741 nan 0.0100 0.9922
## 4 57.8056 nan 0.0100 1.0112
## 5 56.7986 nan 0.0100 0.9769
## 6 55.8752 nan 0.0100 0.9015
## 7 54.8514 nan 0.0100 1.0506
## 8 53.9511 nan 0.0100 0.9219
## 9 52.9724 nan 0.0100 0.9615
## 10 52.0419 nan 0.0100 0.9956
## 20 43.6749 nan 0.0100 0.8140
## 40 31.2287 nan 0.0100 0.5267
## 60 22.5799 nan 0.0100 0.3256
## 80 16.8578 nan 0.0100 0.2478
## 100 12.7729 nan 0.0100 0.1684
## 120 9.9485 nan 0.0100 0.1144
## 140 7.9676 nan 0.0100 0.0683
## 160 6.5100 nan 0.0100 0.0572
## 180 5.4399 nan 0.0100 0.0358
## 200 4.7002 nan 0.0100 0.0276
## 220 4.1695 nan 0.0100 0.0184
## 240 3.7746 nan 0.0100 0.0103
## 260 3.4784 nan 0.0100 0.0007
## 280 3.2470 nan 0.0100 0.0029
## 300 3.0648 nan 0.0100 0.0043
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.9114 nan 0.0100 1.0235
## 2 59.8544 nan 0.0100 1.0829
## 3 58.8256 nan 0.0100 1.0491
## 4 57.8158 nan 0.0100 1.0343
## 5 56.7871 nan 0.0100 1.0397
## 6 55.8776 nan 0.0100 0.9607
## 7 54.8405 nan 0.0100 0.9479
## 8 53.9008 nan 0.0100 1.0173
## 9 52.9865 nan 0.0100 0.8911
## 10 52.0564 nan 0.0100 0.9678
## 20 43.8467 nan 0.0100 0.7355
## 40 31.4376 nan 0.0100 0.5162
## 60 22.9465 nan 0.0100 0.3335
## 80 17.1166 nan 0.0100 0.2320
## 100 13.1458 nan 0.0100 0.1522
## 120 10.3169 nan 0.0100 0.1267
## 140 8.3525 nan 0.0100 0.0663
## 160 6.8938 nan 0.0100 0.0608
## 180 5.8778 nan 0.0100 0.0362
## 200 5.1397 nan 0.0100 0.0247
## 220 4.5938 nan 0.0100 0.0237
## 240 4.1886 nan 0.0100 0.0136
## 260 3.8909 nan 0.0100 0.0114
## 280 3.6706 nan 0.0100 0.0014
## 300 3.4952 nan 0.0100 0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0320 nan 0.0500 3.9036
## 2 54.5431 nan 0.0500 3.1505
## 3 51.3175 nan 0.0500 3.3125
## 4 48.5021 nan 0.0500 2.9328
## 5 45.5377 nan 0.0500 2.7748
## 6 43.2551 nan 0.0500 2.1519
## 7 41.0764 nan 0.0500 2.0129
## 8 38.9532 nan 0.0500 2.1007
## 9 36.6735 nan 0.0500 1.9303
## 10 34.8733 nan 0.0500 1.7155
## 20 21.8982 nan 0.0500 0.9277
## 40 11.0476 nan 0.0500 0.2596
## 60 6.9322 nan 0.0500 0.0827
## 80 5.0555 nan 0.0500 0.0309
## 100 4.1734 nan 0.0500 0.0137
## 120 3.7203 nan 0.0500 0.0069
## 140 3.4903 nan 0.0500 -0.0055
## 160 3.3660 nan 0.0500 -0.0207
## 180 3.2785 nan 0.0500 -0.0133
## 200 3.2111 nan 0.0500 -0.0064
## 220 3.1337 nan 0.0500 -0.0177
## 240 3.0943 nan 0.0500 -0.0157
## 260 3.0177 nan 0.0500 -0.0039
## 280 2.9654 nan 0.0500 -0.0076
## 300 2.9303 nan 0.0500 -0.0175
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8246 nan 0.0500 3.8069
## 2 54.5173 nan 0.0500 3.4161
## 3 51.5395 nan 0.0500 3.3087
## 4 48.6179 nan 0.0500 3.0133
## 5 45.9722 nan 0.0500 2.8870
## 6 43.6940 nan 0.0500 2.3218
## 7 41.3385 nan 0.0500 2.2202
## 8 39.2852 nan 0.0500 2.0523
## 9 37.3543 nan 0.0500 1.7968
## 10 35.5395 nan 0.0500 1.7426
## 20 21.9442 nan 0.0500 1.0094
## 40 11.0329 nan 0.0500 0.2508
## 60 6.8113 nan 0.0500 0.1251
## 80 5.0744 nan 0.0500 0.0393
## 100 4.2370 nan 0.0500 0.0108
## 120 3.8250 nan 0.0500 0.0046
## 140 3.6007 nan 0.0500 -0.0011
## 160 3.4859 nan 0.0500 -0.0121
## 180 3.3932 nan 0.0500 -0.0091
## 200 3.3100 nan 0.0500 -0.0063
## 220 3.2454 nan 0.0500 0.0011
## 240 3.1762 nan 0.0500 -0.0037
## 260 3.1160 nan 0.0500 -0.0041
## 280 3.0701 nan 0.0500 -0.0065
## 300 3.0351 nan 0.0500 -0.0096
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0441 nan 0.0500 3.7926
## 2 54.8287 nan 0.0500 3.6888
## 3 51.5855 nan 0.0500 3.0969
## 4 48.2923 nan 0.0500 2.7331
## 5 45.6297 nan 0.0500 2.7493
## 6 43.1559 nan 0.0500 2.5933
## 7 40.9070 nan 0.0500 2.1149
## 8 38.5866 nan 0.0500 1.9367
## 9 36.6783 nan 0.0500 1.9797
## 10 35.0153 nan 0.0500 1.7954
## 20 22.1595 nan 0.0500 0.9428
## 40 11.1369 nan 0.0500 0.2178
## 60 7.1095 nan 0.0500 0.1208
## 80 5.2042 nan 0.0500 0.0616
## 100 4.3460 nan 0.0500 0.0219
## 120 3.9957 nan 0.0500 -0.0275
## 140 3.8210 nan 0.0500 -0.0185
## 160 3.6894 nan 0.0500 -0.0012
## 180 3.5919 nan 0.0500 -0.0037
## 200 3.5230 nan 0.0500 -0.0092
## 220 3.4410 nan 0.0500 -0.0068
## 240 3.3806 nan 0.0500 -0.0187
## 260 3.3166 nan 0.0500 -0.0033
## 280 3.2670 nan 0.0500 -0.0127
## 300 3.2187 nan 0.0500 -0.0120
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9732 nan 0.0500 5.0766
## 2 52.4679 nan 0.0500 4.3311
## 3 48.3264 nan 0.0500 3.8133
## 4 44.4243 nan 0.0500 3.6729
## 5 40.9426 nan 0.0500 3.7253
## 6 37.8368 nan 0.0500 3.1277
## 7 34.8356 nan 0.0500 2.7551
## 8 32.2039 nan 0.0500 2.4053
## 9 29.7965 nan 0.0500 2.1979
## 10 27.6176 nan 0.0500 2.0831
## 20 14.1842 nan 0.0500 0.8042
## 40 5.7740 nan 0.0500 0.1591
## 60 3.5590 nan 0.0500 0.0044
## 80 2.9460 nan 0.0500 -0.0268
## 100 2.6220 nan 0.0500 -0.0039
## 120 2.3881 nan 0.0500 -0.0067
## 140 2.2174 nan 0.0500 -0.0256
## 160 2.0517 nan 0.0500 -0.0223
## 180 1.9344 nan 0.0500 -0.0076
## 200 1.8351 nan 0.0500 -0.0169
## 220 1.7387 nan 0.0500 -0.0091
## 240 1.6488 nan 0.0500 -0.0120
## 260 1.5618 nan 0.0500 -0.0094
## 280 1.4899 nan 0.0500 -0.0045
## 300 1.4252 nan 0.0500 -0.0073
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7626 nan 0.0500 5.2616
## 2 52.2961 nan 0.0500 4.2161
## 3 48.1645 nan 0.0500 3.6381
## 4 44.2978 nan 0.0500 4.0835
## 5 40.6974 nan 0.0500 3.7189
## 6 37.5823 nan 0.0500 3.0162
## 7 34.7504 nan 0.0500 2.7777
## 8 32.1139 nan 0.0500 2.5326
## 9 29.6980 nan 0.0500 2.4592
## 10 27.4756 nan 0.0500 2.0332
## 20 14.0349 nan 0.0500 0.8806
## 40 5.5720 nan 0.0500 0.1319
## 60 3.5739 nan 0.0500 0.0133
## 80 3.0852 nan 0.0500 -0.0028
## 100 2.7889 nan 0.0500 -0.0090
## 120 2.5724 nan 0.0500 -0.0073
## 140 2.4002 nan 0.0500 -0.0085
## 160 2.2654 nan 0.0500 -0.0123
## 180 2.1400 nan 0.0500 -0.0147
## 200 2.0371 nan 0.0500 -0.0063
## 220 1.9388 nan 0.0500 -0.0162
## 240 1.8495 nan 0.0500 -0.0126
## 260 1.7852 nan 0.0500 -0.0208
## 280 1.7209 nan 0.0500 -0.0100
## 300 1.6538 nan 0.0500 -0.0078
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.9713 nan 0.0500 5.4928
## 2 52.4291 nan 0.0500 4.4699
## 3 48.3727 nan 0.0500 4.0367
## 4 44.4707 nan 0.0500 3.8570
## 5 40.9740 nan 0.0500 3.6530
## 6 37.9045 nan 0.0500 2.9720
## 7 34.8866 nan 0.0500 3.2349
## 8 32.2309 nan 0.0500 2.5453
## 9 29.7234 nan 0.0500 2.0863
## 10 27.7302 nan 0.0500 1.9942
## 20 14.3717 nan 0.0500 0.8875
## 40 5.9060 nan 0.0500 0.1763
## 60 3.9694 nan 0.0500 0.0145
## 80 3.4253 nan 0.0500 -0.0133
## 100 3.1380 nan 0.0500 -0.0205
## 120 2.9462 nan 0.0500 -0.0122
## 140 2.7932 nan 0.0500 -0.0273
## 160 2.6559 nan 0.0500 -0.0077
## 180 2.5309 nan 0.0500 -0.0160
## 200 2.4088 nan 0.0500 -0.0144
## 220 2.3305 nan 0.0500 -0.0130
## 240 2.2463 nan 0.0500 -0.0256
## 260 2.1812 nan 0.0500 -0.0085
## 280 2.0929 nan 0.0500 -0.0079
## 300 2.0246 nan 0.0500 -0.0124
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6527 nan 0.0500 5.4209
## 2 51.3889 nan 0.0500 5.3058
## 3 46.8690 nan 0.0500 4.0886
## 4 42.9162 nan 0.0500 3.8024
## 5 39.3671 nan 0.0500 3.4322
## 6 36.2320 nan 0.0500 3.2942
## 7 33.1884 nan 0.0500 3.0409
## 8 30.5938 nan 0.0500 2.5090
## 9 28.1127 nan 0.0500 2.5185
## 10 25.9688 nan 0.0500 2.2446
## 20 12.3933 nan 0.0500 0.7846
## 40 4.4251 nan 0.0500 0.1171
## 60 2.7606 nan 0.0500 -0.0043
## 80 2.2441 nan 0.0500 -0.0061
## 100 1.9671 nan 0.0500 -0.0125
## 120 1.7889 nan 0.0500 -0.0104
## 140 1.6247 nan 0.0500 -0.0122
## 160 1.4880 nan 0.0500 -0.0014
## 180 1.3664 nan 0.0500 -0.0096
## 200 1.2483 nan 0.0500 -0.0045
## 220 1.1622 nan 0.0500 -0.0060
## 240 1.0733 nan 0.0500 -0.0074
## 260 0.9910 nan 0.0500 -0.0068
## 280 0.9281 nan 0.0500 -0.0112
## 300 0.8563 nan 0.0500 -0.0029
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7463 nan 0.0500 5.5604
## 2 51.8746 nan 0.0500 4.5910
## 3 47.3341 nan 0.0500 4.7281
## 4 43.3017 nan 0.0500 4.4060
## 5 39.7371 nan 0.0500 3.3006
## 6 36.7604 nan 0.0500 3.1659
## 7 33.6199 nan 0.0500 2.5288
## 8 30.9942 nan 0.0500 2.6074
## 9 28.5158 nan 0.0500 2.3772
## 10 26.3997 nan 0.0500 2.1882
## 20 12.6284 nan 0.0500 0.8531
## 40 4.6844 nan 0.0500 0.1644
## 60 3.0655 nan 0.0500 0.0135
## 80 2.5299 nan 0.0500 -0.0102
## 100 2.2663 nan 0.0500 -0.0152
## 120 2.0987 nan 0.0500 -0.0134
## 140 1.9400 nan 0.0500 -0.0008
## 160 1.8088 nan 0.0500 -0.0139
## 180 1.7125 nan 0.0500 -0.0149
## 200 1.5990 nan 0.0500 -0.0219
## 220 1.4907 nan 0.0500 -0.0180
## 240 1.4066 nan 0.0500 -0.0061
## 260 1.3280 nan 0.0500 -0.0065
## 280 1.2644 nan 0.0500 -0.0080
## 300 1.1963 nan 0.0500 -0.0154
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.4938 nan 0.0500 5.2998
## 2 51.8435 nan 0.0500 4.9099
## 3 47.4414 nan 0.0500 3.9309
## 4 43.3504 nan 0.0500 4.2734
## 5 39.7379 nan 0.0500 3.3469
## 6 36.7073 nan 0.0500 3.2462
## 7 33.6378 nan 0.0500 2.9720
## 8 30.9603 nan 0.0500 2.8667
## 9 28.5738 nan 0.0500 2.4097
## 10 26.2968 nan 0.0500 2.2067
## 20 12.8494 nan 0.0500 0.9390
## 40 4.9731 nan 0.0500 0.0948
## 60 3.4319 nan 0.0500 0.0035
## 80 2.9577 nan 0.0500 -0.0232
## 100 2.7100 nan 0.0500 -0.0110
## 120 2.5209 nan 0.0500 -0.0142
## 140 2.3386 nan 0.0500 -0.0088
## 160 2.2338 nan 0.0500 -0.0102
## 180 2.1042 nan 0.0500 -0.0194
## 200 1.9966 nan 0.0500 -0.0109
## 220 1.8962 nan 0.0500 -0.0032
## 240 1.8105 nan 0.0500 -0.0221
## 260 1.7334 nan 0.0500 -0.0075
## 280 1.6411 nan 0.0500 -0.0117
## 300 1.5873 nan 0.0500 -0.0104
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.1977 nan 0.1000 7.4660
## 2 48.2178 nan 0.1000 5.9712
## 3 43.1700 nan 0.1000 4.1692
## 4 38.3877 nan 0.1000 4.4018
## 5 33.9463 nan 0.1000 3.7847
## 6 30.6617 nan 0.1000 2.9239
## 7 27.9505 nan 0.1000 2.6399
## 8 25.3583 nan 0.1000 2.8621
## 9 22.9037 nan 0.1000 2.1542
## 10 20.8359 nan 0.1000 1.9039
## 20 11.0221 nan 0.1000 0.7067
## 40 5.0339 nan 0.1000 0.0895
## 60 3.7857 nan 0.1000 0.0198
## 80 3.4479 nan 0.1000 -0.0242
## 100 3.2798 nan 0.1000 -0.0107
## 120 3.1685 nan 0.1000 -0.0306
## 140 3.0644 nan 0.1000 -0.0347
## 160 2.9826 nan 0.1000 -0.0131
## 180 2.9169 nan 0.1000 -0.0179
## 200 2.8581 nan 0.1000 -0.0097
## 220 2.7914 nan 0.1000 -0.0086
## 240 2.7264 nan 0.1000 -0.0089
## 260 2.6905 nan 0.1000 -0.0141
## 280 2.6638 nan 0.1000 -0.0289
## 300 2.5957 nan 0.1000 -0.0248
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3415 nan 0.1000 7.1067
## 2 48.5181 nan 0.1000 6.2958
## 3 43.2629 nan 0.1000 5.1808
## 4 38.8502 nan 0.1000 4.2454
## 5 35.0131 nan 0.1000 3.6627
## 6 31.6117 nan 0.1000 3.3416
## 7 28.3329 nan 0.1000 2.9352
## 8 25.5805 nan 0.1000 2.5954
## 9 23.3408 nan 0.1000 1.9313
## 10 21.5298 nan 0.1000 1.7782
## 20 10.9913 nan 0.1000 0.4158
## 40 5.1504 nan 0.1000 0.1276
## 60 3.9973 nan 0.1000 0.0090
## 80 3.6917 nan 0.1000 -0.0281
## 100 3.5124 nan 0.1000 -0.0038
## 120 3.3691 nan 0.1000 -0.0213
## 140 3.2594 nan 0.1000 -0.0156
## 160 3.1812 nan 0.1000 -0.0512
## 180 3.0930 nan 0.1000 -0.0121
## 200 3.0201 nan 0.1000 -0.0090
## 220 2.9672 nan 0.1000 -0.0123
## 240 2.9189 nan 0.1000 -0.0117
## 260 2.8583 nan 0.1000 -0.0131
## 280 2.8068 nan 0.1000 -0.0132
## 300 2.7506 nan 0.1000 -0.0132
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.5799 nan 0.1000 7.5566
## 2 48.5670 nan 0.1000 6.2876
## 3 43.3600 nan 0.1000 5.2236
## 4 38.5741 nan 0.1000 4.5952
## 5 34.6908 nan 0.1000 3.5599
## 6 31.3011 nan 0.1000 3.0332
## 7 28.4832 nan 0.1000 2.7306
## 8 25.6532 nan 0.1000 2.6216
## 9 23.4697 nan 0.1000 2.0832
## 10 21.4403 nan 0.1000 1.6425
## 20 11.0070 nan 0.1000 0.4945
## 40 5.2678 nan 0.1000 0.0732
## 60 4.0230 nan 0.1000 0.0062
## 80 3.6998 nan 0.1000 -0.0283
## 100 3.5201 nan 0.1000 -0.0098
## 120 3.3967 nan 0.1000 -0.0081
## 140 3.2490 nan 0.1000 -0.0474
## 160 3.1703 nan 0.1000 -0.0180
## 180 3.1149 nan 0.1000 -0.0271
## 200 3.0444 nan 0.1000 -0.0383
## 220 2.9657 nan 0.1000 -0.0251
## 240 2.8947 nan 0.1000 -0.0369
## 260 2.8454 nan 0.1000 -0.0090
## 280 2.8107 nan 0.1000 -0.0119
## 300 2.7774 nan 0.1000 -0.0130
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.1763 nan 0.1000 10.3046
## 2 44.2383 nan 0.1000 8.2328
## 3 37.3153 nan 0.1000 6.3583
## 4 31.9485 nan 0.1000 5.5184
## 5 27.4657 nan 0.1000 3.9867
## 6 23.3813 nan 0.1000 3.5071
## 7 20.1588 nan 0.1000 3.0212
## 8 17.6364 nan 0.1000 2.6216
## 9 15.5000 nan 0.1000 2.0555
## 10 13.6467 nan 0.1000 1.5339
## 20 5.3586 nan 0.1000 0.3444
## 40 2.9241 nan 0.1000 -0.0112
## 60 2.3350 nan 0.1000 -0.0110
## 80 2.0820 nan 0.1000 -0.0170
## 100 1.8367 nan 0.1000 -0.0142
## 120 1.6791 nan 0.1000 -0.0313
## 140 1.5618 nan 0.1000 -0.0356
## 160 1.4122 nan 0.1000 -0.0147
## 180 1.3137 nan 0.1000 -0.0235
## 200 1.2206 nan 0.1000 -0.0130
## 220 1.1456 nan 0.1000 -0.0122
## 240 1.0858 nan 0.1000 -0.0128
## 260 1.0233 nan 0.1000 -0.0046
## 280 0.9600 nan 0.1000 -0.0083
## 300 0.8859 nan 0.1000 -0.0042
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.3655 nan 0.1000 9.5434
## 2 44.3622 nan 0.1000 7.3696
## 3 38.0453 nan 0.1000 5.7417
## 4 32.6289 nan 0.1000 6.1526
## 5 27.6899 nan 0.1000 4.2952
## 6 23.8558 nan 0.1000 3.8494
## 7 20.4550 nan 0.1000 3.5154
## 8 17.9055 nan 0.1000 2.5476
## 9 15.6375 nan 0.1000 2.0716
## 10 13.9656 nan 0.1000 1.6323
## 20 5.5211 nan 0.1000 0.2734
## 40 3.2364 nan 0.1000 -0.0001
## 60 2.6540 nan 0.1000 0.0034
## 80 2.3800 nan 0.1000 -0.0281
## 100 2.1544 nan 0.1000 -0.0049
## 120 1.9728 nan 0.1000 -0.0159
## 140 1.8236 nan 0.1000 -0.0473
## 160 1.7040 nan 0.1000 -0.0176
## 180 1.6045 nan 0.1000 -0.0202
## 200 1.4994 nan 0.1000 -0.0253
## 220 1.3982 nan 0.1000 -0.0162
## 240 1.3133 nan 0.1000 -0.0178
## 260 1.2456 nan 0.1000 -0.0111
## 280 1.1974 nan 0.1000 -0.0143
## 300 1.1411 nan 0.1000 -0.0130
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4274 nan 0.1000 10.1398
## 2 44.4224 nan 0.1000 8.2069
## 3 38.0346 nan 0.1000 6.8476
## 4 32.5190 nan 0.1000 4.8989
## 5 27.8687 nan 0.1000 3.9048
## 6 24.2671 nan 0.1000 3.4605
## 7 20.8978 nan 0.1000 3.1609
## 8 18.2995 nan 0.1000 2.5340
## 9 16.2640 nan 0.1000 1.8258
## 10 14.3728 nan 0.1000 1.8466
## 20 5.8116 nan 0.1000 0.2398
## 40 3.4061 nan 0.1000 -0.0041
## 60 2.9419 nan 0.1000 -0.0363
## 80 2.6451 nan 0.1000 -0.0209
## 100 2.4001 nan 0.1000 -0.0230
## 120 2.2060 nan 0.1000 -0.0294
## 140 2.0125 nan 0.1000 -0.0084
## 160 1.8804 nan 0.1000 -0.0183
## 180 1.7790 nan 0.1000 -0.0200
## 200 1.6883 nan 0.1000 -0.0227
## 220 1.5905 nan 0.1000 -0.0286
## 240 1.5090 nan 0.1000 -0.0141
## 260 1.4484 nan 0.1000 -0.0111
## 280 1.3860 nan 0.1000 -0.0195
## 300 1.3285 nan 0.1000 -0.0124
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.6338 nan 0.1000 10.6310
## 2 43.1409 nan 0.1000 7.8526
## 3 35.8119 nan 0.1000 6.9085
## 4 30.2242 nan 0.1000 5.5063
## 5 25.1533 nan 0.1000 5.0691
## 6 21.3656 nan 0.1000 3.7887
## 7 18.2957 nan 0.1000 2.9007
## 8 15.7961 nan 0.1000 2.3271
## 9 13.6561 nan 0.1000 2.1348
## 10 11.8394 nan 0.1000 1.6803
## 20 4.3106 nan 0.1000 0.2317
## 40 2.3415 nan 0.1000 -0.0308
## 60 1.8198 nan 0.1000 -0.0168
## 80 1.5360 nan 0.1000 -0.0123
## 100 1.2859 nan 0.1000 -0.0163
## 120 1.0852 nan 0.1000 -0.0188
## 140 0.9553 nan 0.1000 -0.0140
## 160 0.8472 nan 0.1000 -0.0142
## 180 0.7443 nan 0.1000 -0.0144
## 200 0.6633 nan 0.1000 -0.0096
## 220 0.5865 nan 0.1000 -0.0075
## 240 0.5197 nan 0.1000 -0.0067
## 260 0.4666 nan 0.1000 -0.0147
## 280 0.4194 nan 0.1000 -0.0104
## 300 0.3754 nan 0.1000 -0.0051
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.2678 nan 0.1000 10.9446
## 2 42.2272 nan 0.1000 9.5996
## 3 35.6808 nan 0.1000 6.7896
## 4 30.1885 nan 0.1000 5.5910
## 5 25.5106 nan 0.1000 4.6908
## 6 21.6233 nan 0.1000 4.2761
## 7 18.3163 nan 0.1000 3.0600
## 8 15.5537 nan 0.1000 2.3474
## 9 13.7931 nan 0.1000 1.9919
## 10 12.1228 nan 0.1000 1.6952
## 20 4.6926 nan 0.1000 0.1830
## 40 2.7273 nan 0.1000 -0.0374
## 60 2.2169 nan 0.1000 -0.0309
## 80 1.9304 nan 0.1000 -0.0521
## 100 1.7005 nan 0.1000 -0.0588
## 120 1.5040 nan 0.1000 -0.0177
## 140 1.3514 nan 0.1000 -0.0204
## 160 1.2131 nan 0.1000 -0.0137
## 180 1.0999 nan 0.1000 -0.0234
## 200 0.9947 nan 0.1000 -0.0285
## 220 0.8928 nan 0.1000 -0.0045
## 240 0.8237 nan 0.1000 -0.0141
## 260 0.7514 nan 0.1000 -0.0229
## 280 0.6977 nan 0.1000 -0.0150
## 300 0.6397 nan 0.1000 -0.0051
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5243 nan 0.1000 10.3151
## 2 43.5037 nan 0.1000 8.0391
## 3 36.3146 nan 0.1000 6.2526
## 4 30.5839 nan 0.1000 5.7645
## 5 25.9360 nan 0.1000 3.9629
## 6 22.1256 nan 0.1000 3.4614
## 7 18.9333 nan 0.1000 2.8435
## 8 16.1183 nan 0.1000 2.3615
## 9 13.7670 nan 0.1000 2.0181
## 10 12.1843 nan 0.1000 1.6366
## 20 4.7071 nan 0.1000 0.2993
## 40 3.0248 nan 0.1000 -0.0216
## 60 2.5585 nan 0.1000 -0.0063
## 80 2.2927 nan 0.1000 -0.0548
## 100 2.0388 nan 0.1000 -0.0346
## 120 1.8565 nan 0.1000 -0.0123
## 140 1.7084 nan 0.1000 -0.0177
## 160 1.6002 nan 0.1000 -0.0267
## 180 1.4884 nan 0.1000 -0.0169
## 200 1.3894 nan 0.1000 -0.0094
## 220 1.3004 nan 0.1000 -0.0077
## 240 1.2206 nan 0.1000 -0.0155
## 260 1.1393 nan 0.1000 -0.0153
## 280 1.0749 nan 0.1000 -0.0144
## 300 1.0164 nan 0.1000 -0.0108
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.5310 nan 0.0100 0.7276
## 2 58.7274 nan 0.0100 0.7540
## 3 58.0097 nan 0.0100 0.7208
## 4 57.2996 nan 0.0100 0.6594
## 5 56.5967 nan 0.0100 0.6803
## 6 55.9249 nan 0.0100 0.6456
## 7 55.2071 nan 0.0100 0.6612
## 8 54.5822 nan 0.0100 0.6166
## 9 53.9591 nan 0.0100 0.6489
## 10 53.3008 nan 0.0100 0.6375
## 20 47.3029 nan 0.0100 0.5035
## 40 38.1055 nan 0.0100 0.3775
## 60 31.2738 nan 0.0100 0.2812
## 80 26.0247 nan 0.0100 0.2264
## 100 21.9756 nan 0.0100 0.1770
## 120 18.8576 nan 0.0100 0.0893
## 140 16.4067 nan 0.0100 0.0491
## 160 14.3861 nan 0.0100 0.0727
## 180 12.7174 nan 0.0100 0.0818
## 200 11.3596 nan 0.0100 0.0522
## 220 10.2164 nan 0.0100 0.0333
## 240 9.2647 nan 0.0100 0.0190
## 260 8.4918 nan 0.0100 0.0308
## 280 7.8139 nan 0.0100 0.0234
## 300 7.2264 nan 0.0100 0.0205
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4608 nan 0.0100 0.7181
## 2 58.6655 nan 0.0100 0.7044
## 3 57.9136 nan 0.0100 0.7424
## 4 57.2325 nan 0.0100 0.7134
## 5 56.5630 nan 0.0100 0.7258
## 6 55.8832 nan 0.0100 0.6459
## 7 55.2360 nan 0.0100 0.6986
## 8 54.6031 nan 0.0100 0.6654
## 9 53.9255 nan 0.0100 0.6405
## 10 53.2681 nan 0.0100 0.6429
## 20 47.4623 nan 0.0100 0.5270
## 40 38.4321 nan 0.0100 0.3985
## 60 31.5295 nan 0.0100 0.3064
## 80 26.2660 nan 0.0100 0.2518
## 100 22.1788 nan 0.0100 0.1801
## 120 18.9704 nan 0.0100 0.0960
## 140 16.4751 nan 0.0100 0.0947
## 160 14.4683 nan 0.0100 0.0766
## 180 12.7725 nan 0.0100 0.0624
## 200 11.4226 nan 0.0100 0.0514
## 220 10.3344 nan 0.0100 0.0391
## 240 9.3668 nan 0.0100 0.0276
## 260 8.5621 nan 0.0100 0.0299
## 280 7.8826 nan 0.0100 0.0246
## 300 7.2998 nan 0.0100 0.0163
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4931 nan 0.0100 0.7427
## 2 58.7447 nan 0.0100 0.7792
## 3 58.0285 nan 0.0100 0.7712
## 4 57.3424 nan 0.0100 0.7535
## 5 56.6934 nan 0.0100 0.6807
## 6 56.0248 nan 0.0100 0.6733
## 7 55.3520 nan 0.0100 0.6468
## 8 54.6651 nan 0.0100 0.6637
## 9 54.0678 nan 0.0100 0.6154
## 10 53.5092 nan 0.0100 0.6202
## 20 47.6536 nan 0.0100 0.4539
## 40 38.5259 nan 0.0100 0.3714
## 60 31.5008 nan 0.0100 0.2844
## 80 26.2746 nan 0.0100 0.2227
## 100 22.0520 nan 0.0100 0.1755
## 120 18.9481 nan 0.0100 0.1335
## 140 16.5071 nan 0.0100 0.1044
## 160 14.4742 nan 0.0100 0.0697
## 180 12.8642 nan 0.0100 0.0716
## 200 11.5470 nan 0.0100 0.0503
## 220 10.4247 nan 0.0100 0.0510
## 240 9.5027 nan 0.0100 0.0409
## 260 8.7361 nan 0.0100 0.0266
## 280 8.0682 nan 0.0100 0.0226
## 300 7.4985 nan 0.0100 0.0252
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2562 nan 0.0100 0.9495
## 2 58.2776 nan 0.0100 0.8652
## 3 57.2564 nan 0.0100 0.9598
## 4 56.2957 nan 0.0100 0.9446
## 5 55.3454 nan 0.0100 0.8770
## 6 54.4817 nan 0.0100 0.9847
## 7 53.5707 nan 0.0100 0.8120
## 8 52.7068 nan 0.0100 0.9104
## 9 51.8652 nan 0.0100 0.8198
## 10 50.9812 nan 0.0100 0.8332
## 20 43.3049 nan 0.0100 0.6795
## 40 31.8561 nan 0.0100 0.4527
## 60 23.8294 nan 0.0100 0.2563
## 80 18.3028 nan 0.0100 0.1789
## 100 14.3420 nan 0.0100 0.1493
## 120 11.5584 nan 0.0100 0.1105
## 140 9.3850 nan 0.0100 0.0914
## 160 7.8056 nan 0.0100 0.0626
## 180 6.6584 nan 0.0100 0.0417
## 200 5.7627 nan 0.0100 0.0294
## 220 5.1003 nan 0.0100 0.0192
## 240 4.5476 nan 0.0100 0.0150
## 260 4.1614 nan 0.0100 0.0095
## 280 3.8294 nan 0.0100 0.0100
## 300 3.5996 nan 0.0100 0.0057
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2168 nan 0.0100 0.8972
## 2 58.2118 nan 0.0100 1.0036
## 3 57.2523 nan 0.0100 0.9117
## 4 56.3160 nan 0.0100 1.0042
## 5 55.3696 nan 0.0100 0.8439
## 6 54.4433 nan 0.0100 0.7690
## 7 53.5090 nan 0.0100 0.8167
## 8 52.6270 nan 0.0100 0.7579
## 9 51.8101 nan 0.0100 0.8046
## 10 50.9744 nan 0.0100 0.8645
## 20 43.2930 nan 0.0100 0.7352
## 40 31.9709 nan 0.0100 0.4822
## 60 23.9414 nan 0.0100 0.3245
## 80 18.3799 nan 0.0100 0.2293
## 100 14.4788 nan 0.0100 0.1678
## 120 11.6612 nan 0.0100 0.0833
## 140 9.5193 nan 0.0100 0.0848
## 160 7.8943 nan 0.0100 0.0562
## 180 6.7135 nan 0.0100 0.0483
## 200 5.8479 nan 0.0100 0.0352
## 220 5.1854 nan 0.0100 0.0230
## 240 4.6985 nan 0.0100 0.0126
## 260 4.3008 nan 0.0100 0.0111
## 280 4.0089 nan 0.0100 0.0041
## 300 3.7798 nan 0.0100 0.0073
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.2672 nan 0.0100 1.0476
## 2 58.2366 nan 0.0100 1.0227
## 3 57.2547 nan 0.0100 0.8995
## 4 56.2928 nan 0.0100 0.8597
## 5 55.3744 nan 0.0100 0.8448
## 6 54.4875 nan 0.0100 0.8610
## 7 53.6227 nan 0.0100 0.8626
## 8 52.7994 nan 0.0100 0.8165
## 9 51.9697 nan 0.0100 0.8049
## 10 51.1370 nan 0.0100 0.7252
## 20 43.4795 nan 0.0100 0.7595
## 40 32.0588 nan 0.0100 0.4886
## 60 24.0832 nan 0.0100 0.3181
## 80 18.5443 nan 0.0100 0.1998
## 100 14.6046 nan 0.0100 0.1416
## 120 11.8787 nan 0.0100 0.0990
## 140 9.7991 nan 0.0100 0.0829
## 160 8.2642 nan 0.0100 0.0593
## 180 7.0519 nan 0.0100 0.0470
## 200 6.1789 nan 0.0100 0.0273
## 220 5.4887 nan 0.0100 0.0203
## 240 4.9829 nan 0.0100 0.0135
## 260 4.6013 nan 0.0100 0.0079
## 280 4.2946 nan 0.0100 0.0088
## 300 4.0803 nan 0.0100 0.0046
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1660 nan 0.0100 1.0533
## 2 58.1259 nan 0.0100 1.1299
## 3 57.0723 nan 0.0100 1.1273
## 4 56.0621 nan 0.0100 0.8128
## 5 55.0832 nan 0.0100 0.8950
## 6 54.1275 nan 0.0100 0.9705
## 7 53.2139 nan 0.0100 0.8460
## 8 52.2474 nan 0.0100 0.8900
## 9 51.3724 nan 0.0100 0.9093
## 10 50.5048 nan 0.0100 0.9553
## 20 42.4873 nan 0.0100 0.7225
## 40 30.4558 nan 0.0100 0.4640
## 60 22.1976 nan 0.0100 0.3334
## 80 16.3987 nan 0.0100 0.2039
## 100 12.4337 nan 0.0100 0.1314
## 120 9.7016 nan 0.0100 0.1237
## 140 7.7292 nan 0.0100 0.0744
## 160 6.3353 nan 0.0100 0.0539
## 180 5.3284 nan 0.0100 0.0429
## 200 4.5739 nan 0.0100 0.0261
## 220 4.0087 nan 0.0100 0.0227
## 240 3.5926 nan 0.0100 0.0081
## 260 3.2951 nan 0.0100 0.0033
## 280 3.0562 nan 0.0100 0.0021
## 300 2.8713 nan 0.0100 0.0046
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1759 nan 0.0100 1.0092
## 2 58.1542 nan 0.0100 1.0883
## 3 57.1099 nan 0.0100 1.0436
## 4 56.1356 nan 0.0100 0.9374
## 5 55.1770 nan 0.0100 0.8702
## 6 54.2092 nan 0.0100 1.0263
## 7 53.3252 nan 0.0100 0.9006
## 8 52.4076 nan 0.0100 0.9524
## 9 51.5204 nan 0.0100 0.8619
## 10 50.6685 nan 0.0100 0.8119
## 20 42.6965 nan 0.0100 0.7701
## 40 30.5920 nan 0.0100 0.5110
## 60 22.3897 nan 0.0100 0.3454
## 80 16.7214 nan 0.0100 0.2645
## 100 12.6832 nan 0.0100 0.1565
## 120 9.8446 nan 0.0100 0.1136
## 140 7.8585 nan 0.0100 0.0836
## 160 6.4842 nan 0.0100 0.0524
## 180 5.4501 nan 0.0100 0.0426
## 200 4.7094 nan 0.0100 0.0240
## 220 4.1711 nan 0.0100 0.0131
## 240 3.7629 nan 0.0100 0.0041
## 260 3.4784 nan 0.0100 0.0064
## 280 3.2599 nan 0.0100 0.0016
## 300 3.0861 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1830 nan 0.0100 0.9324
## 2 58.1254 nan 0.0100 0.9812
## 3 57.1255 nan 0.0100 0.9642
## 4 56.1621 nan 0.0100 0.9591
## 5 55.2049 nan 0.0100 0.8737
## 6 54.2480 nan 0.0100 0.9627
## 7 53.3274 nan 0.0100 0.8895
## 8 52.4552 nan 0.0100 0.8572
## 9 51.5569 nan 0.0100 0.8378
## 10 50.6552 nan 0.0100 0.8811
## 20 42.6543 nan 0.0100 0.7007
## 40 30.7191 nan 0.0100 0.4778
## 60 22.5604 nan 0.0100 0.3193
## 80 16.8728 nan 0.0100 0.2143
## 100 12.9632 nan 0.0100 0.1605
## 120 10.1685 nan 0.0100 0.1139
## 140 8.2640 nan 0.0100 0.0662
## 160 6.9085 nan 0.0100 0.0561
## 180 5.8667 nan 0.0100 0.0390
## 200 5.1421 nan 0.0100 0.0260
## 220 4.6250 nan 0.0100 0.0159
## 240 4.2565 nan 0.0100 0.0059
## 260 3.9693 nan 0.0100 0.0150
## 280 3.7530 nan 0.0100 0.0036
## 300 3.5810 nan 0.0100 0.0023
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.2172 nan 0.0500 3.5345
## 2 53.0580 nan 0.0500 3.3131
## 3 50.0289 nan 0.0500 3.1580
## 4 47.3257 nan 0.0500 2.4752
## 5 44.7458 nan 0.0500 2.2671
## 6 42.1244 nan 0.0500 2.3969
## 7 39.9029 nan 0.0500 2.2012
## 8 38.0133 nan 0.0500 1.8091
## 9 36.1339 nan 0.0500 1.7790
## 10 34.3214 nan 0.0500 1.8085
## 20 21.9802 nan 0.0500 0.7899
## 40 11.5176 nan 0.0500 0.2089
## 60 7.2705 nan 0.0500 0.0793
## 80 5.3203 nan 0.0500 0.0324
## 100 4.4005 nan 0.0500 0.0020
## 120 3.8871 nan 0.0500 -0.0012
## 140 3.6344 nan 0.0500 0.0057
## 160 3.4908 nan 0.0500 -0.0004
## 180 3.3927 nan 0.0500 -0.0053
## 200 3.2918 nan 0.0500 -0.0019
## 220 3.2162 nan 0.0500 -0.0043
## 240 3.1590 nan 0.0500 -0.0041
## 260 3.0991 nan 0.0500 -0.0087
## 280 3.0532 nan 0.0500 -0.0022
## 300 3.0042 nan 0.0500 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5490 nan 0.0500 3.4438
## 2 53.2862 nan 0.0500 3.4542
## 3 50.4501 nan 0.0500 2.4023
## 4 47.5761 nan 0.0500 2.8047
## 5 44.8229 nan 0.0500 2.5701
## 6 42.2079 nan 0.0500 2.3402
## 7 39.6999 nan 0.0500 2.3014
## 8 37.4855 nan 0.0500 1.9654
## 9 35.5773 nan 0.0500 1.9692
## 10 33.7154 nan 0.0500 1.6105
## 20 21.7557 nan 0.0500 0.8792
## 40 11.3053 nan 0.0500 0.2809
## 60 7.4102 nan 0.0500 0.0932
## 80 5.5168 nan 0.0500 0.0281
## 100 4.6080 nan 0.0500 -0.0069
## 120 4.1156 nan 0.0500 0.0085
## 140 3.8375 nan 0.0500 0.0029
## 160 3.6796 nan 0.0500 -0.0032
## 180 3.5564 nan 0.0500 -0.0047
## 200 3.4547 nan 0.0500 -0.0022
## 220 3.3867 nan 0.0500 -0.0022
## 240 3.3141 nan 0.0500 -0.0023
## 260 3.2498 nan 0.0500 -0.0153
## 280 3.1983 nan 0.0500 -0.0067
## 300 3.1387 nan 0.0500 -0.0061
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.4512 nan 0.0500 3.8397
## 2 53.1691 nan 0.0500 3.3514
## 3 50.0897 nan 0.0500 3.0425
## 4 47.3378 nan 0.0500 2.4967
## 5 44.6938 nan 0.0500 2.4810
## 6 42.4108 nan 0.0500 2.4061
## 7 40.1914 nan 0.0500 1.8212
## 8 38.1419 nan 0.0500 1.9198
## 9 36.3059 nan 0.0500 1.8363
## 10 34.4816 nan 0.0500 1.4315
## 20 21.7611 nan 0.0500 0.9200
## 40 11.2906 nan 0.0500 0.2416
## 60 7.3961 nan 0.0500 0.1041
## 80 5.5350 nan 0.0500 0.0360
## 100 4.6021 nan 0.0500 0.0282
## 120 4.1641 nan 0.0500 0.0127
## 140 3.9733 nan 0.0500 -0.0040
## 160 3.8452 nan 0.0500 0.0015
## 180 3.7473 nan 0.0500 0.0054
## 200 3.6667 nan 0.0500 -0.0151
## 220 3.5911 nan 0.0500 -0.0088
## 240 3.5337 nan 0.0500 -0.0004
## 260 3.4694 nan 0.0500 -0.0093
## 280 3.4218 nan 0.0500 -0.0064
## 300 3.3818 nan 0.0500 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.3105 nan 0.0500 4.6992
## 2 50.8135 nan 0.0500 4.9273
## 3 46.7715 nan 0.0500 3.6046
## 4 43.2418 nan 0.0500 3.4671
## 5 39.8614 nan 0.0500 2.9666
## 6 36.7503 nan 0.0500 2.6636
## 7 34.0464 nan 0.0500 2.6040
## 8 31.5051 nan 0.0500 2.5223
## 9 29.1072 nan 0.0500 2.3144
## 10 26.9775 nan 0.0500 1.7371
## 20 13.9623 nan 0.0500 0.8471
## 40 5.7412 nan 0.0500 0.1827
## 60 3.6319 nan 0.0500 0.0399
## 80 2.9357 nan 0.0500 0.0072
## 100 2.5925 nan 0.0500 -0.0149
## 120 2.3684 nan 0.0500 -0.0146
## 140 2.2093 nan 0.0500 -0.0125
## 160 2.0621 nan 0.0500 -0.0024
## 180 1.9443 nan 0.0500 -0.0153
## 200 1.8580 nan 0.0500 -0.0146
## 220 1.7489 nan 0.0500 -0.0072
## 240 1.6616 nan 0.0500 -0.0111
## 260 1.6007 nan 0.0500 -0.0121
## 280 1.5365 nan 0.0500 -0.0064
## 300 1.4736 nan 0.0500 -0.0054
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.3460 nan 0.0500 4.8082
## 2 51.1024 nan 0.0500 4.1978
## 3 46.9398 nan 0.0500 3.9122
## 4 43.1605 nan 0.0500 3.4750
## 5 39.6809 nan 0.0500 3.5362
## 6 36.7364 nan 0.0500 2.6174
## 7 34.0597 nan 0.0500 2.5813
## 8 31.5075 nan 0.0500 2.3642
## 9 29.3071 nan 0.0500 2.1936
## 10 27.2265 nan 0.0500 1.8889
## 20 14.0143 nan 0.0500 0.8055
## 40 5.7156 nan 0.0500 0.1698
## 60 3.7682 nan 0.0500 0.0161
## 80 3.1659 nan 0.0500 -0.0168
## 100 2.8759 nan 0.0500 0.0044
## 120 2.6875 nan 0.0500 -0.0132
## 140 2.5153 nan 0.0500 -0.0075
## 160 2.3933 nan 0.0500 -0.0151
## 180 2.2462 nan 0.0500 -0.0121
## 200 2.1533 nan 0.0500 -0.0092
## 220 2.0818 nan 0.0500 -0.0031
## 240 1.9915 nan 0.0500 -0.0122
## 260 1.9341 nan 0.0500 -0.0136
## 280 1.8615 nan 0.0500 -0.0121
## 300 1.7806 nan 0.0500 -0.0056
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.4673 nan 0.0500 4.8546
## 2 51.0004 nan 0.0500 4.7062
## 3 46.8684 nan 0.0500 3.8162
## 4 43.1961 nan 0.0500 3.6055
## 5 39.7377 nan 0.0500 3.2091
## 6 36.9828 nan 0.0500 2.8802
## 7 34.2291 nan 0.0500 2.8777
## 8 31.6605 nan 0.0500 2.1394
## 9 29.4957 nan 0.0500 2.4222
## 10 27.3567 nan 0.0500 2.0692
## 20 14.2855 nan 0.0500 0.6694
## 40 6.1202 nan 0.0500 0.1544
## 60 4.1146 nan 0.0500 0.0206
## 80 3.4638 nan 0.0500 -0.0198
## 100 3.1951 nan 0.0500 -0.0115
## 120 3.0209 nan 0.0500 -0.0064
## 140 2.8661 nan 0.0500 -0.0085
## 160 2.7551 nan 0.0500 -0.0093
## 180 2.6418 nan 0.0500 -0.0082
## 200 2.5480 nan 0.0500 -0.0204
## 220 2.4556 nan 0.0500 -0.0033
## 240 2.3835 nan 0.0500 -0.0134
## 260 2.3073 nan 0.0500 -0.0202
## 280 2.2169 nan 0.0500 -0.0109
## 300 2.1496 nan 0.0500 -0.0030
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.1857 nan 0.0500 4.8837
## 2 50.5688 nan 0.0500 4.1611
## 3 46.3689 nan 0.0500 4.7456
## 4 42.4907 nan 0.0500 3.8934
## 5 39.0269 nan 0.0500 3.1725
## 6 35.8638 nan 0.0500 2.9448
## 7 32.9522 nan 0.0500 2.9695
## 8 30.4349 nan 0.0500 2.5099
## 9 28.2084 nan 0.0500 2.2963
## 10 26.1367 nan 0.0500 2.2792
## 20 12.3870 nan 0.0500 0.7144
## 40 4.5159 nan 0.0500 0.1651
## 60 2.8716 nan 0.0500 0.0297
## 80 2.3045 nan 0.0500 -0.0115
## 100 1.9737 nan 0.0500 -0.0124
## 120 1.7930 nan 0.0500 -0.0118
## 140 1.6307 nan 0.0500 -0.0118
## 160 1.4851 nan 0.0500 -0.0045
## 180 1.3631 nan 0.0500 -0.0117
## 200 1.2643 nan 0.0500 -0.0136
## 220 1.1881 nan 0.0500 -0.0091
## 240 1.0961 nan 0.0500 -0.0063
## 260 1.0162 nan 0.0500 -0.0065
## 280 0.9550 nan 0.0500 -0.0126
## 300 0.8910 nan 0.0500 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.9232 nan 0.0500 5.3887
## 2 50.0496 nan 0.0500 4.9480
## 3 45.7527 nan 0.0500 4.1426
## 4 41.8920 nan 0.0500 4.1011
## 5 38.1925 nan 0.0500 3.4646
## 6 35.0637 nan 0.0500 3.4100
## 7 32.3189 nan 0.0500 2.8284
## 8 29.6359 nan 0.0500 2.8051
## 9 27.2683 nan 0.0500 2.7251
## 10 25.3115 nan 0.0500 2.0270
## 20 12.1808 nan 0.0500 0.7856
## 40 4.5920 nan 0.0500 0.0962
## 60 3.0367 nan 0.0500 0.0119
## 80 2.5324 nan 0.0500 -0.0127
## 100 2.2956 nan 0.0500 -0.0173
## 120 2.1016 nan 0.0500 -0.0085
## 140 1.9468 nan 0.0500 -0.0129
## 160 1.8193 nan 0.0500 -0.0090
## 180 1.6931 nan 0.0500 -0.0049
## 200 1.5853 nan 0.0500 -0.0100
## 220 1.4886 nan 0.0500 -0.0144
## 240 1.4078 nan 0.0500 -0.0171
## 260 1.3378 nan 0.0500 -0.0051
## 280 1.2736 nan 0.0500 -0.0133
## 300 1.2098 nan 0.0500 -0.0065
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.3440 nan 0.0500 5.2930
## 2 50.6264 nan 0.0500 4.0565
## 3 46.1377 nan 0.0500 3.7722
## 4 42.3810 nan 0.0500 3.9496
## 5 38.8966 nan 0.0500 3.2894
## 6 35.7395 nan 0.0500 3.0532
## 7 32.7523 nan 0.0500 2.7270
## 8 30.1362 nan 0.0500 2.4489
## 9 27.8266 nan 0.0500 2.5671
## 10 25.8540 nan 0.0500 2.0192
## 20 12.7242 nan 0.0500 0.7560
## 40 4.9571 nan 0.0500 0.0860
## 60 3.5689 nan 0.0500 0.0199
## 80 3.0792 nan 0.0500 -0.0008
## 100 2.7888 nan 0.0500 -0.0108
## 120 2.5822 nan 0.0500 -0.0168
## 140 2.4062 nan 0.0500 -0.0144
## 160 2.2650 nan 0.0500 -0.0160
## 180 2.1387 nan 0.0500 -0.0104
## 200 2.0378 nan 0.0500 -0.0226
## 220 1.9611 nan 0.0500 -0.0134
## 240 1.8733 nan 0.0500 -0.0130
## 260 1.7797 nan 0.0500 -0.0064
## 280 1.7152 nan 0.0500 -0.0095
## 300 1.6319 nan 0.0500 -0.0218
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.4413 nan 0.1000 7.0482
## 2 46.9345 nan 0.1000 6.0775
## 3 42.1806 nan 0.1000 4.9367
## 4 37.7369 nan 0.1000 3.8293
## 5 34.1193 nan 0.1000 3.6720
## 6 30.9113 nan 0.1000 3.1331
## 7 28.2375 nan 0.1000 2.3688
## 8 25.6355 nan 0.1000 2.0058
## 9 23.4848 nan 0.1000 1.9998
## 10 21.5960 nan 0.1000 1.8265
## 20 11.2122 nan 0.1000 0.5703
## 40 5.5294 nan 0.1000 0.0751
## 60 4.0125 nan 0.1000 0.0272
## 80 3.5826 nan 0.1000 -0.0205
## 100 3.3283 nan 0.1000 -0.0180
## 120 3.1888 nan 0.1000 -0.0183
## 140 3.0648 nan 0.1000 0.0043
## 160 2.9676 nan 0.1000 -0.0046
## 180 2.8878 nan 0.1000 -0.0253
## 200 2.8500 nan 0.1000 -0.0109
## 220 2.7893 nan 0.1000 -0.0068
## 240 2.7399 nan 0.1000 -0.0125
## 260 2.6782 nan 0.1000 -0.0181
## 280 2.6396 nan 0.1000 -0.0284
## 300 2.5996 nan 0.1000 -0.0092
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.2113 nan 0.1000 7.2185
## 2 47.1566 nan 0.1000 5.7679
## 3 42.4754 nan 0.1000 4.8436
## 4 38.4897 nan 0.1000 3.7088
## 5 34.5489 nan 0.1000 4.0258
## 6 31.0548 nan 0.1000 3.4631
## 7 28.3184 nan 0.1000 2.6479
## 8 25.9242 nan 0.1000 2.3159
## 9 23.7075 nan 0.1000 2.3142
## 10 21.6945 nan 0.1000 1.4562
## 20 11.0545 nan 0.1000 0.4796
## 40 5.3072 nan 0.1000 0.0443
## 60 4.0285 nan 0.1000 0.0102
## 80 3.6023 nan 0.1000 0.0107
## 100 3.3816 nan 0.1000 -0.0131
## 120 3.2236 nan 0.1000 -0.0061
## 140 3.1159 nan 0.1000 -0.0056
## 160 3.0313 nan 0.1000 -0.0047
## 180 2.9501 nan 0.1000 -0.0023
## 200 2.9048 nan 0.1000 -0.0199
## 220 2.8509 nan 0.1000 -0.0022
## 240 2.7995 nan 0.1000 0.0019
## 260 2.7515 nan 0.1000 -0.0059
## 280 2.7220 nan 0.1000 -0.0070
## 300 2.6959 nan 0.1000 -0.0108
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.8100 nan 0.1000 7.3750
## 2 46.6933 nan 0.1000 5.6288
## 3 42.1308 nan 0.1000 4.7390
## 4 37.9086 nan 0.1000 4.4668
## 5 33.9932 nan 0.1000 3.5485
## 6 30.6513 nan 0.1000 2.8972
## 7 27.9182 nan 0.1000 2.4862
## 8 25.3093 nan 0.1000 2.3595
## 9 23.3493 nan 0.1000 1.8550
## 10 21.4531 nan 0.1000 2.0171
## 20 11.1004 nan 0.1000 0.5512
## 40 5.7002 nan 0.1000 0.0896
## 60 4.5496 nan 0.1000 -0.0396
## 80 4.1940 nan 0.1000 -0.0272
## 100 3.9786 nan 0.1000 0.0008
## 120 3.8166 nan 0.1000 -0.0010
## 140 3.6624 nan 0.1000 -0.0052
## 160 3.5650 nan 0.1000 -0.0129
## 180 3.4765 nan 0.1000 -0.0209
## 200 3.3868 nan 0.1000 -0.0115
## 220 3.3098 nan 0.1000 -0.0210
## 240 3.2425 nan 0.1000 -0.0056
## 260 3.1999 nan 0.1000 -0.0250
## 280 3.1466 nan 0.1000 -0.0046
## 300 3.0790 nan 0.1000 -0.0271
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.1775 nan 0.1000 9.4080
## 2 43.8478 nan 0.1000 7.6566
## 3 37.2354 nan 0.1000 6.7593
## 4 32.0868 nan 0.1000 5.3496
## 5 27.7231 nan 0.1000 3.8389
## 6 23.8354 nan 0.1000 3.4698
## 7 20.7364 nan 0.1000 2.8912
## 8 18.2526 nan 0.1000 2.3685
## 9 16.0634 nan 0.1000 1.8626
## 10 14.3530 nan 0.1000 1.8486
## 20 5.7293 nan 0.1000 0.3496
## 40 3.0684 nan 0.1000 -0.0380
## 60 2.5188 nan 0.1000 -0.0051
## 80 2.1796 nan 0.1000 -0.0119
## 100 1.9204 nan 0.1000 -0.0369
## 120 1.6951 nan 0.1000 -0.0180
## 140 1.5568 nan 0.1000 -0.0047
## 160 1.4297 nan 0.1000 -0.0074
## 180 1.3127 nan 0.1000 -0.0126
## 200 1.1961 nan 0.1000 -0.0097
## 220 1.1163 nan 0.1000 -0.0077
## 240 1.0486 nan 0.1000 -0.0216
## 260 0.9690 nan 0.1000 -0.0203
## 280 0.9110 nan 0.1000 -0.0192
## 300 0.8538 nan 0.1000 -0.0098
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.6595 nan 0.1000 9.9095
## 2 42.9095 nan 0.1000 7.9389
## 3 36.5242 nan 0.1000 6.2546
## 4 31.4446 nan 0.1000 4.7792
## 5 26.5409 nan 0.1000 4.5327
## 6 23.0049 nan 0.1000 3.4396
## 7 20.1019 nan 0.1000 2.6274
## 8 17.6506 nan 0.1000 2.2507
## 9 15.5127 nan 0.1000 1.6599
## 10 13.9107 nan 0.1000 1.5237
## 20 5.8154 nan 0.1000 0.3539
## 40 3.1334 nan 0.1000 -0.0066
## 60 2.6766 nan 0.1000 -0.0162
## 80 2.4024 nan 0.1000 -0.0175
## 100 2.2158 nan 0.1000 -0.0592
## 120 2.0456 nan 0.1000 -0.0226
## 140 1.9040 nan 0.1000 -0.0156
## 160 1.7839 nan 0.1000 -0.0246
## 180 1.6838 nan 0.1000 -0.0288
## 200 1.5742 nan 0.1000 -0.0162
## 220 1.4782 nan 0.1000 -0.0133
## 240 1.4086 nan 0.1000 -0.0134
## 260 1.3391 nan 0.1000 -0.0210
## 280 1.2640 nan 0.1000 -0.0106
## 300 1.1993 nan 0.1000 -0.0079
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4600 nan 0.1000 9.2256
## 2 42.8153 nan 0.1000 7.4669
## 3 36.6060 nan 0.1000 6.2964
## 4 31.3247 nan 0.1000 5.5919
## 5 27.0207 nan 0.1000 4.1177
## 6 23.5415 nan 0.1000 3.0755
## 7 20.6148 nan 0.1000 2.8223
## 8 18.1067 nan 0.1000 1.9492
## 9 15.8204 nan 0.1000 2.0795
## 10 14.2154 nan 0.1000 1.6097
## 20 6.1504 nan 0.1000 0.3035
## 40 3.6257 nan 0.1000 0.0059
## 60 3.1902 nan 0.1000 -0.0181
## 80 2.8918 nan 0.1000 -0.0603
## 100 2.6322 nan 0.1000 -0.0145
## 120 2.4311 nan 0.1000 -0.0105
## 140 2.2845 nan 0.1000 -0.0244
## 160 2.1437 nan 0.1000 -0.0340
## 180 2.0417 nan 0.1000 -0.0266
## 200 1.9398 nan 0.1000 -0.0200
## 220 1.8479 nan 0.1000 -0.0156
## 240 1.7231 nan 0.1000 -0.0198
## 260 1.6472 nan 0.1000 -0.0202
## 280 1.5822 nan 0.1000 -0.0149
## 300 1.5092 nan 0.1000 -0.0172
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.1278 nan 0.1000 10.7639
## 2 41.9169 nan 0.1000 7.7750
## 3 34.8019 nan 0.1000 6.7118
## 4 29.0119 nan 0.1000 5.8775
## 5 24.5851 nan 0.1000 3.9730
## 6 21.0382 nan 0.1000 3.7615
## 7 18.2798 nan 0.1000 2.5555
## 8 15.6987 nan 0.1000 2.2117
## 9 13.7514 nan 0.1000 1.8226
## 10 11.9778 nan 0.1000 1.7120
## 20 4.3139 nan 0.1000 0.2336
## 40 2.4684 nan 0.1000 -0.0115
## 60 1.9714 nan 0.1000 -0.0249
## 80 1.6010 nan 0.1000 -0.0175
## 100 1.3475 nan 0.1000 -0.0084
## 120 1.1572 nan 0.1000 -0.0148
## 140 0.9967 nan 0.1000 -0.0252
## 160 0.8725 nan 0.1000 -0.0107
## 180 0.7739 nan 0.1000 -0.0212
## 200 0.6847 nan 0.1000 -0.0145
## 220 0.6143 nan 0.1000 -0.0090
## 240 0.5348 nan 0.1000 -0.0105
## 260 0.4857 nan 0.1000 -0.0141
## 280 0.4352 nan 0.1000 -0.0110
## 300 0.3886 nan 0.1000 -0.0026
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.6709 nan 0.1000 9.8987
## 2 42.1694 nan 0.1000 7.3982
## 3 35.4002 nan 0.1000 6.2556
## 4 29.6558 nan 0.1000 5.4455
## 5 25.0474 nan 0.1000 4.9400
## 6 21.3173 nan 0.1000 3.3580
## 7 18.3288 nan 0.1000 2.7970
## 8 15.6790 nan 0.1000 2.3312
## 9 13.6829 nan 0.1000 2.0718
## 10 11.9804 nan 0.1000 1.7679
## 20 4.5127 nan 0.1000 0.3171
## 40 2.7647 nan 0.1000 -0.0138
## 60 2.2612 nan 0.1000 -0.0361
## 80 1.9090 nan 0.1000 -0.0370
## 100 1.6473 nan 0.1000 -0.0123
## 120 1.4679 nan 0.1000 -0.0437
## 140 1.3311 nan 0.1000 -0.0131
## 160 1.2084 nan 0.1000 -0.0261
## 180 1.1072 nan 0.1000 -0.0161
## 200 0.9940 nan 0.1000 -0.0125
## 220 0.9182 nan 0.1000 -0.0248
## 240 0.8526 nan 0.1000 -0.0100
## 260 0.7790 nan 0.1000 -0.0075
## 280 0.7199 nan 0.1000 -0.0123
## 300 0.6626 nan 0.1000 -0.0111
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4166 nan 0.1000 9.7871
## 2 42.3009 nan 0.1000 7.8237
## 3 35.7272 nan 0.1000 7.6116
## 4 30.3339 nan 0.1000 5.4545
## 5 25.7600 nan 0.1000 4.6163
## 6 21.9639 nan 0.1000 3.6043
## 7 18.8351 nan 0.1000 2.8494
## 8 16.3420 nan 0.1000 2.4566
## 9 14.3548 nan 0.1000 1.8962
## 10 12.6935 nan 0.1000 1.5300
## 20 5.0710 nan 0.1000 0.2029
## 40 3.1581 nan 0.1000 -0.0205
## 60 2.6147 nan 0.1000 -0.0018
## 80 2.3475 nan 0.1000 -0.0460
## 100 2.1062 nan 0.1000 -0.0165
## 120 1.8971 nan 0.1000 -0.0157
## 140 1.7357 nan 0.1000 -0.0366
## 160 1.6097 nan 0.1000 -0.0175
## 180 1.4851 nan 0.1000 -0.0193
## 200 1.3963 nan 0.1000 -0.0271
## 220 1.3023 nan 0.1000 -0.0240
## 240 1.2026 nan 0.1000 -0.0187
## 260 1.1243 nan 0.1000 -0.0156
## 280 1.0559 nan 0.1000 -0.0148
## 300 0.9832 nan 0.1000 -0.0083
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.2250 nan 0.0100 0.8080
## 2 61.3759 nan 0.0100 0.7540
## 3 60.5950 nan 0.0100 0.7500
## 4 59.8375 nan 0.0100 0.7442
## 5 59.1443 nan 0.0100 0.7453
## 6 58.4077 nan 0.0100 0.7405
## 7 57.6459 nan 0.0100 0.7713
## 8 56.9483 nan 0.0100 0.6874
## 9 56.2531 nan 0.0100 0.7130
## 10 55.5600 nan 0.0100 0.6723
## 20 49.1877 nan 0.0100 0.5552
## 40 39.4816 nan 0.0100 0.3879
## 60 32.2214 nan 0.0100 0.3467
## 80 26.6893 nan 0.0100 0.2106
## 100 22.5130 nan 0.0100 0.1659
## 120 19.3795 nan 0.0100 0.1082
## 140 16.7304 nan 0.0100 0.0808
## 160 14.6506 nan 0.0100 0.0708
## 180 12.8703 nan 0.0100 0.0727
## 200 11.4987 nan 0.0100 0.0539
## 220 10.3751 nan 0.0100 0.0643
## 240 9.4425 nan 0.0100 0.0187
## 260 8.6003 nan 0.0100 0.0313
## 280 7.9025 nan 0.0100 0.0245
## 300 7.3315 nan 0.0100 0.0221
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1775 nan 0.0100 0.7423
## 2 61.3921 nan 0.0100 0.7972
## 3 60.5523 nan 0.0100 0.7621
## 4 59.7922 nan 0.0100 0.7217
## 5 59.0384 nan 0.0100 0.7266
## 6 58.2876 nan 0.0100 0.6224
## 7 57.5883 nan 0.0100 0.7396
## 8 56.8261 nan 0.0100 0.6935
## 9 56.1379 nan 0.0100 0.7154
## 10 55.3895 nan 0.0100 0.6789
## 20 49.0788 nan 0.0100 0.5400
## 40 39.3837 nan 0.0100 0.3810
## 60 32.0102 nan 0.0100 0.2772
## 80 26.4681 nan 0.0100 0.1781
## 100 22.1948 nan 0.0100 0.1660
## 120 18.9428 nan 0.0100 0.1269
## 140 16.3706 nan 0.0100 0.1015
## 160 14.3054 nan 0.0100 0.0745
## 180 12.6997 nan 0.0100 0.0508
## 200 11.3268 nan 0.0100 0.0539
## 220 10.2240 nan 0.0100 0.0358
## 240 9.3001 nan 0.0100 0.0398
## 260 8.5537 nan 0.0100 0.0327
## 280 7.8663 nan 0.0100 0.0213
## 300 7.2647 nan 0.0100 0.0184
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.1290 nan 0.0100 0.8067
## 2 61.3155 nan 0.0100 0.7800
## 3 60.5204 nan 0.0100 0.7802
## 4 59.7427 nan 0.0100 0.7059
## 5 58.9736 nan 0.0100 0.7207
## 6 58.2447 nan 0.0100 0.7615
## 7 57.5576 nan 0.0100 0.7383
## 8 56.8487 nan 0.0100 0.7156
## 9 56.1289 nan 0.0100 0.6327
## 10 55.4179 nan 0.0100 0.6504
## 20 48.9773 nan 0.0100 0.5425
## 40 39.3361 nan 0.0100 0.4192
## 60 32.2009 nan 0.0100 0.2949
## 80 26.6833 nan 0.0100 0.2309
## 100 22.4815 nan 0.0100 0.1388
## 120 19.2056 nan 0.0100 0.1191
## 140 16.6507 nan 0.0100 0.0993
## 160 14.5210 nan 0.0100 0.0797
## 180 12.8835 nan 0.0100 0.0626
## 200 11.4841 nan 0.0100 0.0604
## 220 10.3122 nan 0.0100 0.0395
## 240 9.3963 nan 0.0100 0.0345
## 260 8.5962 nan 0.0100 0.0276
## 280 7.9332 nan 0.0100 0.0344
## 300 7.3494 nan 0.0100 0.0164
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9338 nan 0.0100 1.0558
## 2 60.9236 nan 0.0100 0.9808
## 3 59.9626 nan 0.0100 0.9892
## 4 58.9619 nan 0.0100 1.0130
## 5 58.0657 nan 0.0100 0.8618
## 6 57.0910 nan 0.0100 0.8310
## 7 56.1803 nan 0.0100 0.9299
## 8 55.2932 nan 0.0100 0.8940
## 9 54.3656 nan 0.0100 0.8518
## 10 53.4433 nan 0.0100 0.8041
## 20 45.3767 nan 0.0100 0.6943
## 40 32.9992 nan 0.0100 0.4429
## 60 24.6285 nan 0.0100 0.3374
## 80 18.7587 nan 0.0100 0.2427
## 100 14.6550 nan 0.0100 0.1579
## 120 11.6727 nan 0.0100 0.1054
## 140 9.5370 nan 0.0100 0.0898
## 160 7.9515 nan 0.0100 0.0635
## 180 6.7901 nan 0.0100 0.0389
## 200 5.8707 nan 0.0100 0.0169
## 220 5.2179 nan 0.0100 0.0228
## 240 4.6995 nan 0.0100 0.0198
## 260 4.3069 nan 0.0100 0.0119
## 280 3.9777 nan 0.0100 0.0129
## 300 3.7251 nan 0.0100 0.0042
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9252 nan 0.0100 1.0553
## 2 60.9329 nan 0.0100 1.0044
## 3 59.8752 nan 0.0100 0.8082
## 4 58.8918 nan 0.0100 1.0154
## 5 57.9231 nan 0.0100 0.9801
## 6 56.9689 nan 0.0100 0.9592
## 7 56.0481 nan 0.0100 0.9219
## 8 55.1753 nan 0.0100 0.9613
## 9 54.3024 nan 0.0100 0.8195
## 10 53.4295 nan 0.0100 0.9299
## 20 45.4755 nan 0.0100 0.6437
## 40 33.1642 nan 0.0100 0.4892
## 60 24.6195 nan 0.0100 0.3006
## 80 18.7592 nan 0.0100 0.2456
## 100 14.6244 nan 0.0100 0.1633
## 120 11.7325 nan 0.0100 0.1171
## 140 9.6111 nan 0.0100 0.0894
## 160 8.0001 nan 0.0100 0.0465
## 180 6.8428 nan 0.0100 0.0429
## 200 5.9700 nan 0.0100 0.0296
## 220 5.2908 nan 0.0100 0.0274
## 240 4.7608 nan 0.0100 0.0190
## 260 4.3717 nan 0.0100 0.0144
## 280 4.0627 nan 0.0100 0.0026
## 300 3.8414 nan 0.0100 0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9034 nan 0.0100 1.0957
## 2 60.8535 nan 0.0100 1.1387
## 3 59.8447 nan 0.0100 1.0560
## 4 58.8592 nan 0.0100 1.0239
## 5 57.8631 nan 0.0100 0.8508
## 6 56.8872 nan 0.0100 0.8977
## 7 55.9178 nan 0.0100 0.9576
## 8 55.0172 nan 0.0100 0.8836
## 9 54.1250 nan 0.0100 0.9736
## 10 53.2791 nan 0.0100 0.8746
## 20 45.2750 nan 0.0100 0.6954
## 40 33.1562 nan 0.0100 0.4326
## 60 24.7718 nan 0.0100 0.3034
## 80 18.8867 nan 0.0100 0.2200
## 100 14.7351 nan 0.0100 0.1555
## 120 11.8158 nan 0.0100 0.1176
## 140 9.6989 nan 0.0100 0.0874
## 160 8.1425 nan 0.0100 0.0566
## 180 6.9508 nan 0.0100 0.0434
## 200 6.1180 nan 0.0100 0.0382
## 220 5.4361 nan 0.0100 0.0208
## 240 4.9255 nan 0.0100 0.0163
## 260 4.5287 nan 0.0100 0.0129
## 280 4.2315 nan 0.0100 0.0074
## 300 4.0083 nan 0.0100 0.0056
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8840 nan 0.0100 1.1042
## 2 60.7707 nan 0.0100 1.0476
## 3 59.7050 nan 0.0100 0.9901
## 4 58.6289 nan 0.0100 0.9761
## 5 57.5941 nan 0.0100 1.0617
## 6 56.6050 nan 0.0100 0.8487
## 7 55.5825 nan 0.0100 0.7780
## 8 54.6082 nan 0.0100 0.9778
## 9 53.6413 nan 0.0100 0.8950
## 10 52.7058 nan 0.0100 0.8485
## 20 44.3335 nan 0.0100 0.7864
## 40 31.7128 nan 0.0100 0.5009
## 60 23.0027 nan 0.0100 0.3533
## 80 17.1075 nan 0.0100 0.2456
## 100 12.9496 nan 0.0100 0.1677
## 120 10.0180 nan 0.0100 0.1198
## 140 7.9927 nan 0.0100 0.0788
## 160 6.5216 nan 0.0100 0.0489
## 180 5.4869 nan 0.0100 0.0387
## 200 4.7378 nan 0.0100 0.0282
## 220 4.1741 nan 0.0100 0.0174
## 240 3.7455 nan 0.0100 0.0069
## 260 3.4255 nan 0.0100 0.0118
## 280 3.1851 nan 0.0100 0.0039
## 300 2.9918 nan 0.0100 -0.0033
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9029 nan 0.0100 1.0838
## 2 60.8524 nan 0.0100 1.0358
## 3 59.8081 nan 0.0100 1.0544
## 4 58.7475 nan 0.0100 0.9792
## 5 57.6785 nan 0.0100 1.1190
## 6 56.6316 nan 0.0100 0.9687
## 7 55.6589 nan 0.0100 0.9899
## 8 54.7260 nan 0.0100 0.9178
## 9 53.7906 nan 0.0100 0.9544
## 10 52.8549 nan 0.0100 0.9613
## 20 44.4998 nan 0.0100 0.7896
## 40 31.8305 nan 0.0100 0.5433
## 60 23.1911 nan 0.0100 0.3793
## 80 17.1612 nan 0.0100 0.2516
## 100 13.0597 nan 0.0100 0.1660
## 120 10.1861 nan 0.0100 0.1142
## 140 8.0607 nan 0.0100 0.0813
## 160 6.6720 nan 0.0100 0.0446
## 180 5.6181 nan 0.0100 0.0284
## 200 4.8530 nan 0.0100 0.0289
## 220 4.3298 nan 0.0100 0.0119
## 240 3.9379 nan 0.0100 0.0124
## 260 3.6107 nan 0.0100 0.0093
## 280 3.3905 nan 0.0100 0.0069
## 300 3.2099 nan 0.0100 0.0013
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.8262 nan 0.0100 1.0282
## 2 60.7414 nan 0.0100 0.9300
## 3 59.7287 nan 0.0100 1.0469
## 4 58.7038 nan 0.0100 0.9661
## 5 57.6772 nan 0.0100 1.0138
## 6 56.7184 nan 0.0100 1.0387
## 7 55.7318 nan 0.0100 0.9497
## 8 54.7784 nan 0.0100 0.8475
## 9 53.8206 nan 0.0100 0.9277
## 10 52.8755 nan 0.0100 0.9244
## 20 44.5045 nan 0.0100 0.7444
## 40 31.9332 nan 0.0100 0.4698
## 60 23.2921 nan 0.0100 0.3239
## 80 17.3321 nan 0.0100 0.2311
## 100 13.2045 nan 0.0100 0.1453
## 120 10.3639 nan 0.0100 0.1160
## 140 8.3058 nan 0.0100 0.0827
## 160 6.8461 nan 0.0100 0.0571
## 180 5.8602 nan 0.0100 0.0313
## 200 5.0924 nan 0.0100 0.0289
## 220 4.5788 nan 0.0100 0.0199
## 240 4.1879 nan 0.0100 0.0120
## 260 3.9032 nan 0.0100 0.0097
## 280 3.6794 nan 0.0100 0.0018
## 300 3.5187 nan 0.0100 0.0035
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1992 nan 0.0500 3.7633
## 2 55.8039 nan 0.0500 3.8103
## 3 52.4253 nan 0.0500 3.2032
## 4 49.5985 nan 0.0500 2.9521
## 5 46.9727 nan 0.0500 2.7187
## 6 44.4842 nan 0.0500 2.6494
## 7 42.5803 nan 0.0500 1.9475
## 8 40.0947 nan 0.0500 1.8446
## 9 38.0131 nan 0.0500 1.7675
## 10 36.3670 nan 0.0500 1.8697
## 20 22.7345 nan 0.0500 0.9666
## 40 11.5011 nan 0.0500 0.2560
## 60 7.3001 nan 0.0500 0.1227
## 80 5.4083 nan 0.0500 0.0197
## 100 4.4284 nan 0.0500 0.0123
## 120 3.9571 nan 0.0500 0.0030
## 140 3.7201 nan 0.0500 0.0027
## 160 3.5906 nan 0.0500 -0.0192
## 180 3.4824 nan 0.0500 -0.0068
## 200 3.4019 nan 0.0500 -0.0050
## 220 3.3336 nan 0.0500 -0.0081
## 240 3.2663 nan 0.0500 -0.0091
## 260 3.2168 nan 0.0500 -0.0033
## 280 3.1686 nan 0.0500 -0.0040
## 300 3.1305 nan 0.0500 -0.0039
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.8162 nan 0.0500 4.2199
## 2 54.8643 nan 0.0500 3.4326
## 3 51.5848 nan 0.0500 3.5052
## 4 48.7158 nan 0.0500 2.6763
## 5 46.0633 nan 0.0500 2.8754
## 6 43.5795 nan 0.0500 2.2300
## 7 41.1610 nan 0.0500 2.1220
## 8 38.8422 nan 0.0500 2.0800
## 9 36.7573 nan 0.0500 1.8519
## 10 34.9317 nan 0.0500 1.8425
## 20 21.6887 nan 0.0500 0.7019
## 40 11.0907 nan 0.0500 0.2682
## 60 7.1343 nan 0.0500 0.0684
## 80 5.3020 nan 0.0500 0.0193
## 100 4.4152 nan 0.0500 0.0124
## 120 4.0134 nan 0.0500 0.0157
## 140 3.7967 nan 0.0500 0.0009
## 160 3.6704 nan 0.0500 0.0033
## 180 3.5799 nan 0.0500 0.0016
## 200 3.5053 nan 0.0500 -0.0118
## 220 3.4409 nan 0.0500 -0.0051
## 240 3.3934 nan 0.0500 -0.0114
## 260 3.3438 nan 0.0500 -0.0073
## 280 3.2999 nan 0.0500 -0.0018
## 300 3.2459 nan 0.0500 -0.0071
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.4357 nan 0.0500 3.5867
## 2 55.5791 nan 0.0500 3.6253
## 3 52.4047 nan 0.0500 3.2569
## 4 49.1121 nan 0.0500 2.8698
## 5 46.2663 nan 0.0500 2.7743
## 6 43.5191 nan 0.0500 2.8597
## 7 41.1253 nan 0.0500 1.9907
## 8 38.7613 nan 0.0500 2.2448
## 9 36.6487 nan 0.0500 1.7755
## 10 34.5699 nan 0.0500 1.7496
## 20 22.0686 nan 0.0500 0.8324
## 40 11.3078 nan 0.0500 0.2082
## 60 7.2191 nan 0.0500 0.0738
## 80 5.3958 nan 0.0500 0.0586
## 100 4.5693 nan 0.0500 0.0168
## 120 4.1460 nan 0.0500 0.0076
## 140 3.9373 nan 0.0500 0.0060
## 160 3.8132 nan 0.0500 -0.0176
## 180 3.7193 nan 0.0500 0.0002
## 200 3.6579 nan 0.0500 -0.0018
## 220 3.5893 nan 0.0500 -0.0002
## 240 3.5393 nan 0.0500 -0.0145
## 260 3.4872 nan 0.0500 -0.0031
## 280 3.4536 nan 0.0500 -0.0060
## 300 3.4053 nan 0.0500 -0.0050
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7751 nan 0.0500 5.4923
## 2 52.9072 nan 0.0500 4.4656
## 3 48.8432 nan 0.0500 4.1989
## 4 44.9278 nan 0.0500 4.0555
## 5 41.5002 nan 0.0500 3.5311
## 6 38.2291 nan 0.0500 3.2110
## 7 35.3868 nan 0.0500 2.9413
## 8 32.6454 nan 0.0500 2.7635
## 9 30.3262 nan 0.0500 2.3333
## 10 28.0794 nan 0.0500 2.0681
## 20 14.2866 nan 0.0500 0.7889
## 40 5.7829 nan 0.0500 0.1991
## 60 3.6033 nan 0.0500 0.0332
## 80 2.9941 nan 0.0500 0.0098
## 100 2.6870 nan 0.0500 -0.0096
## 120 2.5260 nan 0.0500 -0.0056
## 140 2.3545 nan 0.0500 0.0009
## 160 2.2110 nan 0.0500 -0.0150
## 180 2.0876 nan 0.0500 -0.0171
## 200 1.9880 nan 0.0500 -0.0159
## 220 1.8922 nan 0.0500 -0.0096
## 240 1.8132 nan 0.0500 -0.0145
## 260 1.7284 nan 0.0500 -0.0101
## 280 1.6496 nan 0.0500 -0.0081
## 300 1.5754 nan 0.0500 -0.0166
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7875 nan 0.0500 5.2558
## 2 53.1611 nan 0.0500 4.5811
## 3 48.8136 nan 0.0500 4.1785
## 4 44.9329 nan 0.0500 3.3738
## 5 41.6542 nan 0.0500 3.0806
## 6 38.4826 nan 0.0500 2.9178
## 7 35.4393 nan 0.0500 3.0682
## 8 32.7793 nan 0.0500 2.6241
## 9 30.3208 nan 0.0500 2.4860
## 10 28.1855 nan 0.0500 1.9496
## 20 14.4514 nan 0.0500 0.8085
## 40 5.9206 nan 0.0500 0.1137
## 60 3.9843 nan 0.0500 0.0292
## 80 3.3317 nan 0.0500 -0.0128
## 100 3.0584 nan 0.0500 -0.0193
## 120 2.8309 nan 0.0500 -0.0124
## 140 2.6867 nan 0.0500 -0.0184
## 160 2.5395 nan 0.0500 -0.0179
## 180 2.4297 nan 0.0500 -0.0101
## 200 2.3229 nan 0.0500 -0.0101
## 220 2.2192 nan 0.0500 -0.0077
## 240 2.1290 nan 0.0500 -0.0175
## 260 2.0491 nan 0.0500 -0.0108
## 280 1.9831 nan 0.0500 -0.0050
## 300 1.9239 nan 0.0500 -0.0077
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8154 nan 0.0500 4.7682
## 2 53.2368 nan 0.0500 4.7700
## 3 49.1382 nan 0.0500 4.2446
## 4 45.3072 nan 0.0500 3.7644
## 5 41.8973 nan 0.0500 3.4410
## 6 38.6171 nan 0.0500 2.9502
## 7 35.6895 nan 0.0500 2.8480
## 8 33.1026 nan 0.0500 2.4932
## 9 30.5648 nan 0.0500 2.1850
## 10 28.5676 nan 0.0500 2.1028
## 20 14.6785 nan 0.0500 0.8945
## 40 5.9461 nan 0.0500 0.1669
## 60 4.0232 nan 0.0500 0.0260
## 80 3.4272 nan 0.0500 -0.0083
## 100 3.1486 nan 0.0500 -0.0021
## 120 2.9701 nan 0.0500 -0.0131
## 140 2.8023 nan 0.0500 -0.0153
## 160 2.6882 nan 0.0500 -0.0219
## 180 2.5674 nan 0.0500 -0.0103
## 200 2.4610 nan 0.0500 -0.0146
## 220 2.3808 nan 0.0500 -0.0063
## 240 2.3006 nan 0.0500 -0.0073
## 260 2.2280 nan 0.0500 -0.0071
## 280 2.1721 nan 0.0500 -0.0162
## 300 2.1116 nan 0.0500 -0.0159
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.5632 nan 0.0500 5.6227
## 2 52.3703 nan 0.0500 5.1648
## 3 47.7481 nan 0.0500 4.3406
## 4 43.6565 nan 0.0500 4.5368
## 5 40.0569 nan 0.0500 2.9786
## 6 36.6729 nan 0.0500 3.5080
## 7 33.5570 nan 0.0500 2.9001
## 8 30.7604 nan 0.0500 2.6136
## 9 28.1671 nan 0.0500 2.3857
## 10 26.0534 nan 0.0500 1.7687
## 20 12.2958 nan 0.0500 0.7652
## 40 4.5136 nan 0.0500 0.1056
## 60 2.9138 nan 0.0500 0.0070
## 80 2.3907 nan 0.0500 -0.0314
## 100 2.0729 nan 0.0500 -0.0151
## 120 1.8810 nan 0.0500 -0.0203
## 140 1.7020 nan 0.0500 -0.0083
## 160 1.5390 nan 0.0500 -0.0087
## 180 1.4004 nan 0.0500 -0.0052
## 200 1.2840 nan 0.0500 -0.0032
## 220 1.1975 nan 0.0500 -0.0032
## 240 1.0984 nan 0.0500 -0.0135
## 260 1.0196 nan 0.0500 -0.0085
## 280 0.9531 nan 0.0500 -0.0084
## 300 0.8882 nan 0.0500 -0.0063
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.5982 nan 0.0500 5.1419
## 2 52.7043 nan 0.0500 5.2285
## 3 48.2017 nan 0.0500 4.5196
## 4 44.0392 nan 0.0500 4.2655
## 5 40.4676 nan 0.0500 3.4111
## 6 37.1336 nan 0.0500 3.1229
## 7 34.1011 nan 0.0500 3.1204
## 8 31.2700 nan 0.0500 2.4294
## 9 28.8297 nan 0.0500 2.3109
## 10 26.6413 nan 0.0500 1.9269
## 20 12.5885 nan 0.0500 0.8037
## 40 4.6634 nan 0.0500 0.1170
## 60 3.2042 nan 0.0500 0.0020
## 80 2.7327 nan 0.0500 -0.0121
## 100 2.4669 nan 0.0500 -0.0063
## 120 2.2736 nan 0.0500 -0.0336
## 140 2.1018 nan 0.0500 -0.0174
## 160 1.9830 nan 0.0500 -0.0081
## 180 1.8582 nan 0.0500 -0.0162
## 200 1.7204 nan 0.0500 -0.0102
## 220 1.6135 nan 0.0500 -0.0182
## 240 1.5285 nan 0.0500 -0.0076
## 260 1.4476 nan 0.0500 -0.0189
## 280 1.3701 nan 0.0500 -0.0123
## 300 1.3030 nan 0.0500 -0.0074
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8167 nan 0.0500 5.4355
## 2 52.9151 nan 0.0500 4.9638
## 3 48.5077 nan 0.0500 4.4417
## 4 44.4762 nan 0.0500 4.2603
## 5 40.9281 nan 0.0500 3.1023
## 6 37.9542 nan 0.0500 3.2136
## 7 34.8536 nan 0.0500 3.1168
## 8 32.0196 nan 0.0500 2.7030
## 9 29.3508 nan 0.0500 2.3860
## 10 27.1544 nan 0.0500 2.1126
## 20 13.3726 nan 0.0500 0.8988
## 40 5.1666 nan 0.0500 0.1075
## 60 3.5493 nan 0.0500 -0.0049
## 80 3.0793 nan 0.0500 -0.0079
## 100 2.8443 nan 0.0500 -0.0295
## 120 2.6534 nan 0.0500 -0.0263
## 140 2.4687 nan 0.0500 -0.0089
## 160 2.3371 nan 0.0500 -0.0117
## 180 2.2094 nan 0.0500 -0.0177
## 200 2.0858 nan 0.0500 -0.0164
## 220 1.9686 nan 0.0500 -0.0107
## 240 1.8652 nan 0.0500 -0.0170
## 260 1.7810 nan 0.0500 -0.0092
## 280 1.7019 nan 0.0500 -0.0055
## 300 1.6366 nan 0.0500 -0.0249
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.8709 nan 0.1000 7.4844
## 2 48.8909 nan 0.1000 7.1654
## 3 43.3184 nan 0.1000 5.2283
## 4 38.6295 nan 0.1000 4.1805
## 5 34.9287 nan 0.1000 3.4725
## 6 31.7173 nan 0.1000 2.5913
## 7 29.3212 nan 0.1000 2.0871
## 8 26.7942 nan 0.1000 2.3069
## 9 24.3090 nan 0.1000 2.2137
## 10 22.3158 nan 0.1000 1.4802
## 20 11.1699 nan 0.1000 0.7516
## 40 5.3493 nan 0.1000 0.0571
## 60 4.1689 nan 0.1000 0.0173
## 80 3.7641 nan 0.1000 -0.0021
## 100 3.5711 nan 0.1000 -0.0369
## 120 3.4760 nan 0.1000 -0.0062
## 140 3.3479 nan 0.1000 -0.0188
## 160 3.2690 nan 0.1000 -0.0184
## 180 3.1594 nan 0.1000 -0.0287
## 200 3.1050 nan 0.1000 -0.0311
## 220 3.0130 nan 0.1000 -0.0134
## 240 2.9732 nan 0.1000 -0.0111
## 260 2.9131 nan 0.1000 -0.0225
## 280 2.8605 nan 0.1000 -0.0082
## 300 2.8109 nan 0.1000 -0.0155
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0976 nan 0.1000 7.6380
## 2 48.9094 nan 0.1000 6.1164
## 3 43.9099 nan 0.1000 5.2759
## 4 38.8845 nan 0.1000 5.1913
## 5 35.0466 nan 0.1000 3.4813
## 6 31.1977 nan 0.1000 3.8546
## 7 28.5896 nan 0.1000 1.9545
## 8 25.9762 nan 0.1000 2.2206
## 9 23.6819 nan 0.1000 2.2322
## 10 21.8832 nan 0.1000 1.7804
## 20 11.1010 nan 0.1000 0.5533
## 40 5.4764 nan 0.1000 0.0533
## 60 4.1226 nan 0.1000 0.0117
## 80 3.8003 nan 0.1000 0.0030
## 100 3.6072 nan 0.1000 -0.0292
## 120 3.4601 nan 0.1000 -0.0065
## 140 3.3756 nan 0.1000 -0.0112
## 160 3.3047 nan 0.1000 -0.0079
## 180 3.2359 nan 0.1000 -0.0255
## 200 3.1807 nan 0.1000 -0.0178
## 220 3.1151 nan 0.1000 -0.0087
## 240 3.0685 nan 0.1000 -0.0178
## 260 3.0173 nan 0.1000 -0.0198
## 280 2.9703 nan 0.1000 -0.0207
## 300 2.9332 nan 0.1000 -0.0272
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0434 nan 0.1000 6.9573
## 2 48.9979 nan 0.1000 6.3344
## 3 43.4028 nan 0.1000 5.1258
## 4 39.5284 nan 0.1000 3.9916
## 5 35.5220 nan 0.1000 3.7960
## 6 32.4224 nan 0.1000 2.6873
## 7 29.2578 nan 0.1000 3.7091
## 8 26.1247 nan 0.1000 2.6075
## 9 23.9631 nan 0.1000 1.9019
## 10 22.1365 nan 0.1000 1.9129
## 20 11.0655 nan 0.1000 0.4904
## 40 5.5943 nan 0.1000 0.1175
## 60 4.4320 nan 0.1000 -0.0231
## 80 4.1626 nan 0.1000 0.0019
## 100 3.9434 nan 0.1000 -0.0215
## 120 3.7856 nan 0.1000 -0.0157
## 140 3.6685 nan 0.1000 -0.0130
## 160 3.5767 nan 0.1000 -0.0338
## 180 3.4809 nan 0.1000 -0.0247
## 200 3.4153 nan 0.1000 -0.0260
## 220 3.3637 nan 0.1000 -0.0184
## 240 3.2997 nan 0.1000 -0.0220
## 260 3.2196 nan 0.1000 -0.0037
## 280 3.1752 nan 0.1000 -0.0276
## 300 3.1396 nan 0.1000 -0.0097
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.1825 nan 0.1000 8.8474
## 2 45.3544 nan 0.1000 8.0821
## 3 38.3531 nan 0.1000 6.8722
## 4 32.8831 nan 0.1000 5.5796
## 5 28.0763 nan 0.1000 4.7544
## 6 23.9805 nan 0.1000 4.0557
## 7 20.7236 nan 0.1000 2.8624
## 8 18.0459 nan 0.1000 2.3807
## 9 15.7382 nan 0.1000 2.2804
## 10 13.6443 nan 0.1000 1.9852
## 20 5.6796 nan 0.1000 0.2747
## 40 3.1328 nan 0.1000 -0.0065
## 60 2.6449 nan 0.1000 -0.0377
## 80 2.2908 nan 0.1000 -0.0236
## 100 2.0302 nan 0.1000 -0.0200
## 120 1.8393 nan 0.1000 -0.0223
## 140 1.6754 nan 0.1000 -0.0144
## 160 1.5510 nan 0.1000 -0.0144
## 180 1.4331 nan 0.1000 -0.0157
## 200 1.3177 nan 0.1000 -0.0295
## 220 1.2008 nan 0.1000 -0.0218
## 240 1.1064 nan 0.1000 -0.0262
## 260 1.0342 nan 0.1000 -0.0147
## 280 0.9668 nan 0.1000 -0.0105
## 300 0.9057 nan 0.1000 -0.0095
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.5868 nan 0.1000 9.8999
## 2 45.1692 nan 0.1000 8.3824
## 3 38.1811 nan 0.1000 7.8841
## 4 32.2389 nan 0.1000 6.2928
## 5 27.7172 nan 0.1000 4.2075
## 6 23.9832 nan 0.1000 3.9251
## 7 20.5821 nan 0.1000 2.7446
## 8 18.3428 nan 0.1000 2.7741
## 9 15.9871 nan 0.1000 2.0383
## 10 14.1993 nan 0.1000 1.6286
## 20 5.8069 nan 0.1000 0.3173
## 40 3.3438 nan 0.1000 -0.0011
## 60 2.8205 nan 0.1000 -0.0395
## 80 2.5355 nan 0.1000 -0.0163
## 100 2.3143 nan 0.1000 0.0067
## 120 2.1039 nan 0.1000 -0.0054
## 140 1.9622 nan 0.1000 -0.0239
## 160 1.8067 nan 0.1000 -0.0193
## 180 1.7008 nan 0.1000 -0.0135
## 200 1.5884 nan 0.1000 -0.0142
## 220 1.5108 nan 0.1000 -0.0246
## 240 1.4212 nan 0.1000 -0.0177
## 260 1.3480 nan 0.1000 -0.0240
## 280 1.2914 nan 0.1000 -0.0068
## 300 1.2170 nan 0.1000 -0.0182
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0748 nan 0.1000 10.6753
## 2 44.7872 nan 0.1000 8.1787
## 3 38.2032 nan 0.1000 6.4469
## 4 32.6412 nan 0.1000 5.0470
## 5 27.9569 nan 0.1000 4.3828
## 6 24.0892 nan 0.1000 4.1616
## 7 20.7450 nan 0.1000 3.2831
## 8 18.3223 nan 0.1000 2.6124
## 9 16.1706 nan 0.1000 2.0841
## 10 14.3961 nan 0.1000 1.6339
## 20 6.1383 nan 0.1000 0.1825
## 40 3.5999 nan 0.1000 -0.0380
## 60 3.1642 nan 0.1000 -0.0276
## 80 2.8800 nan 0.1000 -0.0243
## 100 2.6763 nan 0.1000 -0.0254
## 120 2.4645 nan 0.1000 -0.0148
## 140 2.3007 nan 0.1000 -0.0425
## 160 2.1856 nan 0.1000 -0.0465
## 180 2.0540 nan 0.1000 -0.0054
## 200 1.9323 nan 0.1000 -0.0259
## 220 1.8353 nan 0.1000 -0.0195
## 240 1.7186 nan 0.1000 -0.0106
## 260 1.6306 nan 0.1000 -0.0263
## 280 1.5604 nan 0.1000 -0.0124
## 300 1.4810 nan 0.1000 -0.0175
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.5608 nan 0.1000 10.3111
## 2 43.9035 nan 0.1000 9.1471
## 3 36.7392 nan 0.1000 7.2499
## 4 31.0089 nan 0.1000 5.8657
## 5 26.3105 nan 0.1000 4.5241
## 6 22.4058 nan 0.1000 3.9436
## 7 19.1915 nan 0.1000 3.0491
## 8 16.4741 nan 0.1000 2.5795
## 9 14.2474 nan 0.1000 2.2784
## 10 12.5049 nan 0.1000 1.7786
## 20 4.6624 nan 0.1000 0.2582
## 40 2.5818 nan 0.1000 -0.0066
## 60 1.9679 nan 0.1000 -0.0325
## 80 1.6002 nan 0.1000 -0.0226
## 100 1.3540 nan 0.1000 -0.0161
## 120 1.1498 nan 0.1000 -0.0136
## 140 0.9919 nan 0.1000 -0.0267
## 160 0.8499 nan 0.1000 -0.0157
## 180 0.7349 nan 0.1000 -0.0061
## 200 0.6403 nan 0.1000 -0.0099
## 220 0.5614 nan 0.1000 -0.0153
## 240 0.5055 nan 0.1000 -0.0072
## 260 0.4534 nan 0.1000 -0.0073
## 280 0.4083 nan 0.1000 -0.0065
## 300 0.3733 nan 0.1000 -0.0071
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.3284 nan 0.1000 10.0724
## 2 43.5374 nan 0.1000 8.9578
## 3 36.9045 nan 0.1000 6.5754
## 4 31.3420 nan 0.1000 5.9663
## 5 26.6040 nan 0.1000 4.1599
## 6 22.5944 nan 0.1000 3.6266
## 7 19.2239 nan 0.1000 3.1525
## 8 16.4007 nan 0.1000 3.0384
## 9 14.1583 nan 0.1000 2.1247
## 10 12.3126 nan 0.1000 1.8650
## 20 4.5941 nan 0.1000 0.2321
## 40 2.6499 nan 0.1000 -0.0497
## 60 2.2690 nan 0.1000 -0.0326
## 80 1.9554 nan 0.1000 -0.0047
## 100 1.7331 nan 0.1000 -0.0390
## 120 1.5406 nan 0.1000 -0.0152
## 140 1.3897 nan 0.1000 -0.0130
## 160 1.2579 nan 0.1000 -0.0224
## 180 1.1366 nan 0.1000 -0.0260
## 200 1.0442 nan 0.1000 -0.0227
## 220 0.9432 nan 0.1000 -0.0127
## 240 0.8643 nan 0.1000 -0.0111
## 260 0.7949 nan 0.1000 -0.0077
## 280 0.7388 nan 0.1000 -0.0090
## 300 0.6876 nan 0.1000 -0.0085
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.8004 nan 0.1000 10.4799
## 2 43.9093 nan 0.1000 8.7984
## 3 36.8988 nan 0.1000 7.5465
## 4 31.4247 nan 0.1000 5.5004
## 5 26.8221 nan 0.1000 4.8386
## 6 22.7944 nan 0.1000 3.6298
## 7 19.6499 nan 0.1000 2.9841
## 8 17.0872 nan 0.1000 2.5629
## 9 15.0596 nan 0.1000 1.7730
## 10 13.2765 nan 0.1000 1.8008
## 20 5.2457 nan 0.1000 0.2893
## 40 3.1964 nan 0.1000 -0.0233
## 60 2.7338 nan 0.1000 -0.0321
## 80 2.4244 nan 0.1000 -0.0454
## 100 2.1530 nan 0.1000 -0.0122
## 120 1.9598 nan 0.1000 -0.0248
## 140 1.7813 nan 0.1000 -0.0071
## 160 1.6445 nan 0.1000 -0.0306
## 180 1.5270 nan 0.1000 -0.0250
## 200 1.4495 nan 0.1000 -0.0274
## 220 1.3159 nan 0.1000 -0.0140
## 240 1.2103 nan 0.1000 -0.0149
## 260 1.1202 nan 0.1000 -0.0155
## 280 1.0456 nan 0.1000 -0.0115
## 300 0.9960 nan 0.1000 -0.0219
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9368 nan 0.0100 0.6958
## 2 58.2282 nan 0.0100 0.6959
## 3 57.5163 nan 0.0100 0.6871
## 4 56.8017 nan 0.0100 0.7084
## 5 56.0913 nan 0.0100 0.6493
## 6 55.3699 nan 0.0100 0.6966
## 7 54.7054 nan 0.0100 0.7111
## 8 54.0419 nan 0.0100 0.7034
## 9 53.4059 nan 0.0100 0.6037
## 10 52.7683 nan 0.0100 0.5773
## 20 46.7715 nan 0.0100 0.4481
## 40 37.7370 nan 0.0100 0.3473
## 60 30.6736 nan 0.0100 0.2820
## 80 25.3878 nan 0.0100 0.2176
## 100 21.3613 nan 0.0100 0.1826
## 120 18.2616 nan 0.0100 0.1124
## 140 15.8383 nan 0.0100 0.0775
## 160 13.8920 nan 0.0100 0.0826
## 180 12.3446 nan 0.0100 0.0589
## 200 11.0718 nan 0.0100 0.0580
## 220 9.9955 nan 0.0100 0.0406
## 240 9.0836 nan 0.0100 0.0363
## 260 8.3120 nan 0.0100 0.0118
## 280 7.6393 nan 0.0100 0.0271
## 300 7.0795 nan 0.0100 0.0251
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.9345 nan 0.0100 0.7421
## 2 58.1976 nan 0.0100 0.7083
## 3 57.4544 nan 0.0100 0.7288
## 4 56.7585 nan 0.0100 0.7630
## 5 56.0923 nan 0.0100 0.6877
## 6 55.3795 nan 0.0100 0.6640
## 7 54.7033 nan 0.0100 0.6739
## 8 54.0282 nan 0.0100 0.6526
## 9 53.4135 nan 0.0100 0.6435
## 10 52.7848 nan 0.0100 0.6709
## 20 47.0669 nan 0.0100 0.5421
## 40 38.0184 nan 0.0100 0.4092
## 60 30.9632 nan 0.0100 0.2853
## 80 25.7020 nan 0.0100 0.2138
## 100 21.6669 nan 0.0100 0.1600
## 120 18.4841 nan 0.0100 0.1230
## 140 16.0269 nan 0.0100 0.0938
## 160 14.0636 nan 0.0100 0.0904
## 180 12.4889 nan 0.0100 0.0623
## 200 11.1804 nan 0.0100 0.0535
## 220 10.0825 nan 0.0100 0.0487
## 240 9.1349 nan 0.0100 0.0537
## 260 8.3593 nan 0.0100 0.0275
## 280 7.7144 nan 0.0100 0.0223
## 300 7.1423 nan 0.0100 0.0184
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.8447 nan 0.0100 0.7519
## 2 58.1389 nan 0.0100 0.7079
## 3 57.4214 nan 0.0100 0.7265
## 4 56.6957 nan 0.0100 0.7188
## 5 55.9591 nan 0.0100 0.7129
## 6 55.2185 nan 0.0100 0.7421
## 7 54.5820 nan 0.0100 0.6987
## 8 53.9502 nan 0.0100 0.6532
## 9 53.2727 nan 0.0100 0.6037
## 10 52.7122 nan 0.0100 0.5277
## 20 47.0099 nan 0.0100 0.5173
## 40 37.9019 nan 0.0100 0.3892
## 60 31.0371 nan 0.0100 0.2787
## 80 25.7268 nan 0.0100 0.2188
## 100 21.6772 nan 0.0100 0.1497
## 120 18.5327 nan 0.0100 0.1388
## 140 16.1403 nan 0.0100 0.0981
## 160 14.1824 nan 0.0100 0.0732
## 180 12.5550 nan 0.0100 0.0619
## 200 11.1649 nan 0.0100 0.0580
## 220 10.0838 nan 0.0100 0.0428
## 240 9.1792 nan 0.0100 0.0310
## 260 8.4321 nan 0.0100 0.0224
## 280 7.7832 nan 0.0100 0.0271
## 300 7.2327 nan 0.0100 0.0193
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6534 nan 0.0100 1.0062
## 2 57.6516 nan 0.0100 0.9429
## 3 56.7025 nan 0.0100 0.9350
## 4 55.7856 nan 0.0100 0.9256
## 5 54.9393 nan 0.0100 0.9323
## 6 54.0453 nan 0.0100 0.8522
## 7 53.2068 nan 0.0100 0.9016
## 8 52.3138 nan 0.0100 0.8958
## 9 51.5176 nan 0.0100 0.7822
## 10 50.7209 nan 0.0100 0.8833
## 20 43.2229 nan 0.0100 0.6959
## 40 31.5793 nan 0.0100 0.4079
## 60 23.5262 nan 0.0100 0.3021
## 80 18.0266 nan 0.0100 0.1920
## 100 14.1394 nan 0.0100 0.1813
## 120 11.3292 nan 0.0100 0.1141
## 140 9.2786 nan 0.0100 0.0624
## 160 7.7394 nan 0.0100 0.0552
## 180 6.6288 nan 0.0100 0.0350
## 200 5.7798 nan 0.0100 0.0308
## 220 5.1309 nan 0.0100 0.0164
## 240 4.6390 nan 0.0100 0.0177
## 260 4.2494 nan 0.0100 0.0089
## 280 3.9517 nan 0.0100 0.0077
## 300 3.7216 nan 0.0100 0.0054
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6537 nan 0.0100 0.9949
## 2 57.7285 nan 0.0100 0.8895
## 3 56.7543 nan 0.0100 0.9134
## 4 55.8228 nan 0.0100 1.0075
## 5 54.8791 nan 0.0100 0.8516
## 6 53.9636 nan 0.0100 0.8997
## 7 53.0311 nan 0.0100 0.9704
## 8 52.1466 nan 0.0100 0.9019
## 9 51.2903 nan 0.0100 0.7765
## 10 50.4514 nan 0.0100 0.8287
## 20 42.9765 nan 0.0100 0.6812
## 40 31.4786 nan 0.0100 0.5159
## 60 23.5451 nan 0.0100 0.3427
## 80 17.9372 nan 0.0100 0.1988
## 100 14.0338 nan 0.0100 0.1616
## 120 11.3038 nan 0.0100 0.1158
## 140 9.2408 nan 0.0100 0.0710
## 160 7.7725 nan 0.0100 0.0555
## 180 6.6545 nan 0.0100 0.0426
## 200 5.7857 nan 0.0100 0.0283
## 220 5.1513 nan 0.0100 0.0212
## 240 4.6650 nan 0.0100 0.0137
## 260 4.3122 nan 0.0100 0.0116
## 280 4.0165 nan 0.0100 0.0088
## 300 3.8111 nan 0.0100 0.0060
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6552 nan 0.0100 0.9249
## 2 57.6478 nan 0.0100 1.0111
## 3 56.6581 nan 0.0100 1.0011
## 4 55.7095 nan 0.0100 0.9740
## 5 54.7816 nan 0.0100 0.9540
## 6 53.8327 nan 0.0100 1.0074
## 7 52.9336 nan 0.0100 0.8186
## 8 52.0684 nan 0.0100 0.7978
## 9 51.2438 nan 0.0100 0.8293
## 10 50.4099 nan 0.0100 0.7810
## 20 42.9934 nan 0.0100 0.6995
## 40 31.6879 nan 0.0100 0.4722
## 60 23.7871 nan 0.0100 0.3308
## 80 18.2452 nan 0.0100 0.2423
## 100 14.4221 nan 0.0100 0.1412
## 120 11.6408 nan 0.0100 0.0893
## 140 9.6140 nan 0.0100 0.0632
## 160 8.1201 nan 0.0100 0.0480
## 180 6.9933 nan 0.0100 0.0473
## 200 6.1425 nan 0.0100 0.0244
## 220 5.4989 nan 0.0100 0.0263
## 240 5.0032 nan 0.0100 0.0205
## 260 4.6434 nan 0.0100 0.0101
## 280 4.3591 nan 0.0100 0.0067
## 300 4.1404 nan 0.0100 0.0028
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6215 nan 0.0100 0.9360
## 2 57.5973 nan 0.0100 1.0649
## 3 56.5718 nan 0.0100 0.9783
## 4 55.6346 nan 0.0100 0.9597
## 5 54.6489 nan 0.0100 0.9505
## 6 53.6826 nan 0.0100 0.8583
## 7 52.7759 nan 0.0100 0.8184
## 8 51.8215 nan 0.0100 0.8624
## 9 50.8656 nan 0.0100 0.9234
## 10 49.9881 nan 0.0100 0.8717
## 20 42.0980 nan 0.0100 0.7069
## 40 30.0272 nan 0.0100 0.4499
## 60 21.8002 nan 0.0100 0.3024
## 80 16.3335 nan 0.0100 0.1964
## 100 12.4237 nan 0.0100 0.1491
## 120 9.7270 nan 0.0100 0.1098
## 140 7.8229 nan 0.0100 0.0581
## 160 6.4332 nan 0.0100 0.0460
## 180 5.4406 nan 0.0100 0.0214
## 200 4.7048 nan 0.0100 0.0166
## 220 4.1639 nan 0.0100 0.0161
## 240 3.7474 nan 0.0100 0.0091
## 260 3.4471 nan 0.0100 0.0060
## 280 3.2346 nan 0.0100 -0.0014
## 300 3.0543 nan 0.0100 0.0030
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6047 nan 0.0100 0.9812
## 2 57.6138 nan 0.0100 1.0773
## 3 56.5962 nan 0.0100 0.9424
## 4 55.5893 nan 0.0100 0.8490
## 5 54.5629 nan 0.0100 0.8462
## 6 53.5969 nan 0.0100 0.9303
## 7 52.6481 nan 0.0100 0.8918
## 8 51.7491 nan 0.0100 0.9577
## 9 50.8389 nan 0.0100 0.9734
## 10 49.9636 nan 0.0100 0.8165
## 20 42.1400 nan 0.0100 0.6951
## 40 30.2455 nan 0.0100 0.4716
## 60 22.0958 nan 0.0100 0.3687
## 80 16.4790 nan 0.0100 0.2389
## 100 12.4753 nan 0.0100 0.1586
## 120 9.7095 nan 0.0100 0.1091
## 140 7.7975 nan 0.0100 0.0659
## 160 6.4313 nan 0.0100 0.0449
## 180 5.4768 nan 0.0100 0.0254
## 200 4.7724 nan 0.0100 0.0253
## 220 4.2325 nan 0.0100 0.0134
## 240 3.8623 nan 0.0100 0.0124
## 260 3.5654 nan 0.0100 0.0033
## 280 3.3369 nan 0.0100 0.0006
## 300 3.1697 nan 0.0100 0.0020
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.6271 nan 0.0100 0.8861
## 2 57.5992 nan 0.0100 1.0159
## 3 56.5539 nan 0.0100 1.1194
## 4 55.6044 nan 0.0100 0.9860
## 5 54.6918 nan 0.0100 0.8781
## 6 53.7361 nan 0.0100 0.9336
## 7 52.8073 nan 0.0100 0.7909
## 8 51.9111 nan 0.0100 0.7630
## 9 51.0111 nan 0.0100 0.8385
## 10 50.1078 nan 0.0100 0.9061
## 20 42.2308 nan 0.0100 0.6613
## 40 30.3481 nan 0.0100 0.4266
## 60 22.1665 nan 0.0100 0.3193
## 80 16.5886 nan 0.0100 0.2666
## 100 12.8261 nan 0.0100 0.1298
## 120 10.1455 nan 0.0100 0.0981
## 140 8.2573 nan 0.0100 0.0791
## 160 6.9054 nan 0.0100 0.0444
## 180 5.9115 nan 0.0100 0.0329
## 200 5.1973 nan 0.0100 0.0261
## 220 4.6745 nan 0.0100 0.0171
## 240 4.2928 nan 0.0100 0.0069
## 260 4.0051 nan 0.0100 0.0054
## 280 3.7803 nan 0.0100 0.0014
## 300 3.6225 nan 0.0100 0.0025
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7455 nan 0.0500 3.9885
## 2 52.3909 nan 0.0500 3.4541
## 3 49.6441 nan 0.0500 2.4246
## 4 46.8784 nan 0.0500 2.8210
## 5 44.2155 nan 0.0500 2.5158
## 6 41.7622 nan 0.0500 2.2067
## 7 39.6512 nan 0.0500 2.1829
## 8 37.6377 nan 0.0500 1.6979
## 9 35.6221 nan 0.0500 2.1787
## 10 33.7747 nan 0.0500 1.6337
## 20 21.4593 nan 0.0500 0.8620
## 40 11.1655 nan 0.0500 0.2403
## 60 6.9941 nan 0.0500 0.1245
## 80 5.2472 nan 0.0500 0.0057
## 100 4.3751 nan 0.0500 0.0243
## 120 3.9301 nan 0.0500 -0.0243
## 140 3.7095 nan 0.0500 -0.0018
## 160 3.6022 nan 0.0500 0.0036
## 180 3.5058 nan 0.0500 -0.0025
## 200 3.4432 nan 0.0500 -0.0149
## 220 3.3800 nan 0.0500 -0.0012
## 240 3.3194 nan 0.0500 -0.0089
## 260 3.2774 nan 0.0500 -0.0240
## 280 3.2239 nan 0.0500 -0.0091
## 300 3.1706 nan 0.0500 0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1470 nan 0.0500 3.6240
## 2 52.7097 nan 0.0500 3.6194
## 3 49.8643 nan 0.0500 2.3946
## 4 46.7450 nan 0.0500 2.8934
## 5 44.2464 nan 0.0500 2.4908
## 6 41.6984 nan 0.0500 2.2906
## 7 39.5403 nan 0.0500 2.3850
## 8 37.4274 nan 0.0500 2.0538
## 9 35.4990 nan 0.0500 1.8143
## 10 33.5412 nan 0.0500 1.7861
## 20 21.2077 nan 0.0500 0.7627
## 40 10.8542 nan 0.0500 0.2377
## 60 7.0048 nan 0.0500 0.0641
## 80 5.2486 nan 0.0500 0.0531
## 100 4.4565 nan 0.0500 0.0244
## 120 4.0000 nan 0.0500 0.0092
## 140 3.8053 nan 0.0500 -0.0192
## 160 3.6794 nan 0.0500 -0.0194
## 180 3.5938 nan 0.0500 -0.0015
## 200 3.5247 nan 0.0500 -0.0008
## 220 3.4575 nan 0.0500 -0.0005
## 240 3.3980 nan 0.0500 -0.0083
## 260 3.3367 nan 0.0500 -0.0058
## 280 3.2924 nan 0.0500 -0.0107
## 300 3.2441 nan 0.0500 -0.0063
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.8003 nan 0.0500 3.6979
## 2 52.4842 nan 0.0500 3.2202
## 3 49.3210 nan 0.0500 2.9657
## 4 46.8059 nan 0.0500 2.1259
## 5 44.2487 nan 0.0500 2.7197
## 6 41.7047 nan 0.0500 2.2589
## 7 39.4682 nan 0.0500 2.1230
## 8 37.2456 nan 0.0500 2.1317
## 9 35.4520 nan 0.0500 1.9088
## 10 33.7465 nan 0.0500 1.6150
## 20 21.8730 nan 0.0500 0.9580
## 40 11.1305 nan 0.0500 0.3069
## 60 7.1215 nan 0.0500 0.1033
## 80 5.3705 nan 0.0500 0.0294
## 100 4.5417 nan 0.0500 0.0160
## 120 4.1591 nan 0.0500 -0.0060
## 140 3.9792 nan 0.0500 0.0003
## 160 3.8654 nan 0.0500 -0.0049
## 180 3.7677 nan 0.0500 -0.0092
## 200 3.6709 nan 0.0500 -0.0112
## 220 3.5893 nan 0.0500 -0.0045
## 240 3.5239 nan 0.0500 -0.0071
## 260 3.4592 nan 0.0500 -0.0048
## 280 3.4050 nan 0.0500 -0.0089
## 300 3.3521 nan 0.0500 -0.0084
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.6431 nan 0.0500 4.9729
## 2 50.3649 nan 0.0500 4.1650
## 3 46.4633 nan 0.0500 3.9962
## 4 42.6439 nan 0.0500 3.4716
## 5 39.1465 nan 0.0500 3.0613
## 6 36.1308 nan 0.0500 3.1025
## 7 33.2877 nan 0.0500 2.2693
## 8 30.8152 nan 0.0500 2.6181
## 9 28.6140 nan 0.0500 1.9878
## 10 26.5724 nan 0.0500 1.9819
## 20 13.7432 nan 0.0500 0.9163
## 40 5.7383 nan 0.0500 0.1554
## 60 3.7928 nan 0.0500 0.0238
## 80 3.1485 nan 0.0500 -0.0034
## 100 2.8603 nan 0.0500 -0.0127
## 120 2.6383 nan 0.0500 -0.0008
## 140 2.4793 nan 0.0500 -0.0328
## 160 2.3070 nan 0.0500 -0.0274
## 180 2.1608 nan 0.0500 -0.0084
## 200 2.0373 nan 0.0500 -0.0014
## 220 1.9326 nan 0.0500 -0.0114
## 240 1.8379 nan 0.0500 -0.0115
## 260 1.7367 nan 0.0500 -0.0045
## 280 1.6568 nan 0.0500 -0.0048
## 300 1.5973 nan 0.0500 -0.0081
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.0196 nan 0.0500 4.6119
## 2 50.8345 nan 0.0500 4.1510
## 3 46.7612 nan 0.0500 3.7373
## 4 43.0339 nan 0.0500 3.1780
## 5 39.8748 nan 0.0500 3.0077
## 6 36.6364 nan 0.0500 2.9471
## 7 33.8056 nan 0.0500 2.5013
## 8 31.2879 nan 0.0500 2.3027
## 9 28.9631 nan 0.0500 1.8076
## 10 26.9609 nan 0.0500 2.0095
## 20 13.8211 nan 0.0500 0.8349
## 40 5.8828 nan 0.0500 0.0954
## 60 3.8975 nan 0.0500 0.0086
## 80 3.3065 nan 0.0500 -0.0112
## 100 3.0233 nan 0.0500 -0.0038
## 120 2.8387 nan 0.0500 -0.0094
## 140 2.6716 nan 0.0500 -0.0180
## 160 2.5129 nan 0.0500 -0.0338
## 180 2.4064 nan 0.0500 -0.0118
## 200 2.2939 nan 0.0500 -0.0132
## 220 2.1910 nan 0.0500 -0.0083
## 240 2.1030 nan 0.0500 -0.0186
## 260 2.0391 nan 0.0500 -0.0136
## 280 1.9786 nan 0.0500 -0.0257
## 300 1.9144 nan 0.0500 -0.0086
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.6565 nan 0.0500 4.9930
## 2 50.0634 nan 0.0500 4.2518
## 3 45.9514 nan 0.0500 3.4755
## 4 42.2227 nan 0.0500 3.5463
## 5 38.9535 nan 0.0500 2.9727
## 6 36.0928 nan 0.0500 3.0717
## 7 33.4569 nan 0.0500 2.7614
## 8 30.8881 nan 0.0500 2.3115
## 9 28.7637 nan 0.0500 2.1251
## 10 26.7774 nan 0.0500 2.0122
## 20 14.4285 nan 0.0500 0.8656
## 40 6.1414 nan 0.0500 0.1275
## 60 4.1800 nan 0.0500 0.0202
## 80 3.6274 nan 0.0500 -0.0161
## 100 3.3072 nan 0.0500 -0.0053
## 120 3.1024 nan 0.0500 -0.0137
## 140 2.9503 nan 0.0500 -0.0127
## 160 2.8079 nan 0.0500 -0.0123
## 180 2.6939 nan 0.0500 -0.0130
## 200 2.5996 nan 0.0500 -0.0085
## 220 2.5037 nan 0.0500 -0.0212
## 240 2.4287 nan 0.0500 -0.0105
## 260 2.3524 nan 0.0500 -0.0111
## 280 2.2838 nan 0.0500 -0.0182
## 300 2.2256 nan 0.0500 -0.0294
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.6937 nan 0.0500 4.5151
## 2 50.4994 nan 0.0500 4.4749
## 3 46.0594 nan 0.0500 4.6181
## 4 42.2246 nan 0.0500 3.7210
## 5 38.6145 nan 0.0500 3.3517
## 6 35.4594 nan 0.0500 3.0589
## 7 32.5716 nan 0.0500 3.0667
## 8 30.0517 nan 0.0500 2.5425
## 9 27.8618 nan 0.0500 2.2512
## 10 25.6538 nan 0.0500 2.1904
## 20 12.3599 nan 0.0500 0.9173
## 40 4.6041 nan 0.0500 0.0833
## 60 2.9897 nan 0.0500 -0.0045
## 80 2.4872 nan 0.0500 -0.0106
## 100 2.1966 nan 0.0500 -0.0273
## 120 1.9864 nan 0.0500 -0.0176
## 140 1.7963 nan 0.0500 -0.0179
## 160 1.6543 nan 0.0500 -0.0153
## 180 1.5219 nan 0.0500 -0.0010
## 200 1.3963 nan 0.0500 -0.0133
## 220 1.2837 nan 0.0500 -0.0061
## 240 1.1931 nan 0.0500 -0.0131
## 260 1.1063 nan 0.0500 -0.0039
## 280 1.0308 nan 0.0500 -0.0109
## 300 0.9593 nan 0.0500 -0.0099
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3349 nan 0.0500 4.6946
## 2 49.6219 nan 0.0500 5.1064
## 3 45.5924 nan 0.0500 3.8907
## 4 41.9220 nan 0.0500 3.3193
## 5 38.6614 nan 0.0500 3.4101
## 6 35.5781 nan 0.0500 3.2242
## 7 32.5910 nan 0.0500 3.0844
## 8 30.0082 nan 0.0500 2.4565
## 9 27.6492 nan 0.0500 2.1421
## 10 25.5429 nan 0.0500 2.2653
## 20 12.1288 nan 0.0500 0.7503
## 40 4.6470 nan 0.0500 0.1056
## 60 3.1391 nan 0.0500 0.0011
## 80 2.6168 nan 0.0500 -0.0160
## 100 2.3787 nan 0.0500 -0.0212
## 120 2.1827 nan 0.0500 -0.0093
## 140 2.0216 nan 0.0500 -0.0102
## 160 1.8625 nan 0.0500 -0.0148
## 180 1.7366 nan 0.0500 -0.0176
## 200 1.6237 nan 0.0500 -0.0088
## 220 1.5395 nan 0.0500 -0.0069
## 240 1.4601 nan 0.0500 -0.0049
## 260 1.3888 nan 0.0500 -0.0097
## 280 1.3241 nan 0.0500 -0.0140
## 300 1.2637 nan 0.0500 -0.0151
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.5929 nan 0.0500 5.4908
## 2 49.9664 nan 0.0500 3.9270
## 3 45.7726 nan 0.0500 4.1623
## 4 41.9654 nan 0.0500 3.7929
## 5 38.6443 nan 0.0500 3.4393
## 6 35.4581 nan 0.0500 3.0428
## 7 32.7175 nan 0.0500 2.7409
## 8 30.2537 nan 0.0500 2.5740
## 9 28.0862 nan 0.0500 2.2820
## 10 26.0135 nan 0.0500 1.9770
## 20 12.5471 nan 0.0500 0.8101
## 40 5.1348 nan 0.0500 0.1186
## 60 3.6932 nan 0.0500 0.0164
## 80 3.2272 nan 0.0500 -0.0119
## 100 2.9629 nan 0.0500 -0.0061
## 120 2.7384 nan 0.0500 -0.0190
## 140 2.5361 nan 0.0500 -0.0130
## 160 2.3896 nan 0.0500 -0.0201
## 180 2.2547 nan 0.0500 -0.0141
## 200 2.1351 nan 0.0500 -0.0123
## 220 2.0283 nan 0.0500 -0.0114
## 240 1.9462 nan 0.0500 -0.0106
## 260 1.8627 nan 0.0500 -0.0144
## 280 1.7964 nan 0.0500 -0.0110
## 300 1.7311 nan 0.0500 -0.0090
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.9839 nan 0.1000 6.9754
## 2 47.1768 nan 0.1000 5.6638
## 3 42.1405 nan 0.1000 4.9206
## 4 37.9827 nan 0.1000 4.2613
## 5 33.9113 nan 0.1000 3.1185
## 6 30.4651 nan 0.1000 3.3412
## 7 27.7503 nan 0.1000 2.7547
## 8 24.9550 nan 0.1000 2.5442
## 9 22.8597 nan 0.1000 2.0980
## 10 20.8382 nan 0.1000 1.8342
## 20 10.9077 nan 0.1000 0.4873
## 40 5.1754 nan 0.1000 0.1255
## 60 3.9780 nan 0.1000 0.0361
## 80 3.6496 nan 0.1000 -0.0010
## 100 3.5213 nan 0.1000 -0.0211
## 120 3.3883 nan 0.1000 -0.0142
## 140 3.3020 nan 0.1000 -0.0138
## 160 3.2203 nan 0.1000 -0.0216
## 180 3.1605 nan 0.1000 -0.0152
## 200 3.0923 nan 0.1000 -0.0198
## 220 3.0479 nan 0.1000 -0.0279
## 240 2.9893 nan 0.1000 -0.0241
## 260 2.9363 nan 0.1000 -0.0234
## 280 2.8916 nan 0.1000 -0.0058
## 300 2.8653 nan 0.1000 -0.0224
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4362 nan 0.1000 7.0030
## 2 46.6116 nan 0.1000 5.6361
## 3 42.0642 nan 0.1000 4.5335
## 4 37.7826 nan 0.1000 4.1554
## 5 33.8582 nan 0.1000 3.5498
## 6 30.6153 nan 0.1000 3.1498
## 7 27.8829 nan 0.1000 2.6934
## 8 25.2753 nan 0.1000 2.4935
## 9 23.1285 nan 0.1000 1.8595
## 10 20.9749 nan 0.1000 1.8857
## 20 10.9571 nan 0.1000 0.6122
## 40 5.3502 nan 0.1000 0.0779
## 60 4.1763 nan 0.1000 -0.0137
## 80 3.8024 nan 0.1000 0.0007
## 100 3.6066 nan 0.1000 -0.0004
## 120 3.4560 nan 0.1000 -0.0168
## 140 3.3229 nan 0.1000 -0.0074
## 160 3.2409 nan 0.1000 -0.0217
## 180 3.1511 nan 0.1000 -0.0155
## 200 3.0869 nan 0.1000 -0.0089
## 220 3.0271 nan 0.1000 -0.0201
## 240 2.9514 nan 0.1000 -0.0077
## 260 2.8977 nan 0.1000 -0.0229
## 280 2.8568 nan 0.1000 -0.0198
## 300 2.8289 nan 0.1000 -0.0302
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.0270 nan 0.1000 6.8942
## 2 46.1065 nan 0.1000 5.8935
## 3 41.0751 nan 0.1000 4.6510
## 4 36.9179 nan 0.1000 3.5711
## 5 32.7213 nan 0.1000 4.1847
## 6 30.0405 nan 0.1000 2.7369
## 7 27.1135 nan 0.1000 3.0482
## 8 24.6044 nan 0.1000 2.2309
## 9 22.4807 nan 0.1000 1.8250
## 10 20.5759 nan 0.1000 1.5399
## 20 11.0405 nan 0.1000 0.6537
## 40 5.4706 nan 0.1000 0.0742
## 60 4.4475 nan 0.1000 -0.0146
## 80 4.1196 nan 0.1000 -0.0074
## 100 3.9276 nan 0.1000 -0.0098
## 120 3.7744 nan 0.1000 -0.0933
## 140 3.6593 nan 0.1000 -0.0394
## 160 3.5600 nan 0.1000 -0.0037
## 180 3.4577 nan 0.1000 -0.0154
## 200 3.3640 nan 0.1000 -0.0251
## 220 3.2994 nan 0.1000 -0.0099
## 240 3.2379 nan 0.1000 -0.0178
## 260 3.1911 nan 0.1000 -0.0169
## 280 3.1438 nan 0.1000 -0.0178
## 300 3.0936 nan 0.1000 -0.0188
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.3739 nan 0.1000 9.3698
## 2 42.4066 nan 0.1000 7.9101
## 3 35.9464 nan 0.1000 6.1595
## 4 30.7062 nan 0.1000 4.9506
## 5 26.4514 nan 0.1000 4.3995
## 6 22.7113 nan 0.1000 3.8440
## 7 19.7809 nan 0.1000 2.9080
## 8 17.1170 nan 0.1000 2.4644
## 9 15.0812 nan 0.1000 1.8732
## 10 13.5011 nan 0.1000 1.6270
## 20 5.6571 nan 0.1000 0.3023
## 40 3.2089 nan 0.1000 0.0003
## 60 2.7183 nan 0.1000 -0.1074
## 80 2.3969 nan 0.1000 -0.0318
## 100 2.1714 nan 0.1000 -0.0427
## 120 1.9679 nan 0.1000 -0.0297
## 140 1.7991 nan 0.1000 -0.0175
## 160 1.6459 nan 0.1000 -0.0251
## 180 1.5194 nan 0.1000 -0.0260
## 200 1.4013 nan 0.1000 -0.0079
## 220 1.3234 nan 0.1000 -0.0060
## 240 1.2112 nan 0.1000 -0.0197
## 260 1.1298 nan 0.1000 -0.0158
## 280 1.0584 nan 0.1000 -0.0079
## 300 0.9908 nan 0.1000 -0.0168
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.1423 nan 0.1000 9.6209
## 2 42.2315 nan 0.1000 7.6145
## 3 35.5985 nan 0.1000 6.9319
## 4 30.6417 nan 0.1000 4.9830
## 5 26.4719 nan 0.1000 4.1577
## 6 23.1671 nan 0.1000 3.0891
## 7 20.0749 nan 0.1000 2.8474
## 8 17.4301 nan 0.1000 2.4057
## 9 15.2617 nan 0.1000 2.2254
## 10 13.4821 nan 0.1000 1.4544
## 20 5.7217 nan 0.1000 0.2402
## 40 3.2569 nan 0.1000 0.0051
## 60 2.8185 nan 0.1000 -0.0257
## 80 2.4921 nan 0.1000 -0.0244
## 100 2.2173 nan 0.1000 -0.0101
## 120 2.0746 nan 0.1000 -0.0171
## 140 1.9363 nan 0.1000 -0.0171
## 160 1.8394 nan 0.1000 -0.0283
## 180 1.7516 nan 0.1000 -0.0347
## 200 1.6615 nan 0.1000 -0.0339
## 220 1.5769 nan 0.1000 -0.0080
## 240 1.4851 nan 0.1000 -0.0231
## 260 1.4077 nan 0.1000 -0.0137
## 280 1.3438 nan 0.1000 -0.0256
## 300 1.2647 nan 0.1000 -0.0199
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.4355 nan 0.1000 9.1791
## 2 42.7613 nan 0.1000 8.2567
## 3 36.2901 nan 0.1000 6.3323
## 4 30.8923 nan 0.1000 4.7599
## 5 26.5866 nan 0.1000 3.7903
## 6 22.9649 nan 0.1000 3.4182
## 7 19.7538 nan 0.1000 2.9222
## 8 17.4859 nan 0.1000 2.0773
## 9 15.3692 nan 0.1000 1.5229
## 10 13.7406 nan 0.1000 1.5391
## 20 5.9899 nan 0.1000 0.1866
## 40 3.6544 nan 0.1000 -0.0130
## 60 3.1821 nan 0.1000 -0.0339
## 80 2.9197 nan 0.1000 -0.0033
## 100 2.7338 nan 0.1000 -0.0081
## 120 2.5220 nan 0.1000 -0.0506
## 140 2.4259 nan 0.1000 -0.0428
## 160 2.3044 nan 0.1000 -0.0218
## 180 2.1652 nan 0.1000 -0.0086
## 200 2.0658 nan 0.1000 -0.0306
## 220 1.9594 nan 0.1000 -0.0150
## 240 1.8706 nan 0.1000 -0.0244
## 260 1.7823 nan 0.1000 -0.0135
## 280 1.7086 nan 0.1000 -0.0217
## 300 1.6343 nan 0.1000 -0.0141
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.0358 nan 0.1000 9.6039
## 2 41.9071 nan 0.1000 8.7358
## 3 35.3512 nan 0.1000 6.0948
## 4 29.8506 nan 0.1000 5.4915
## 5 24.9486 nan 0.1000 4.1676
## 6 21.3502 nan 0.1000 3.7699
## 7 18.3742 nan 0.1000 3.0507
## 8 15.7390 nan 0.1000 2.5529
## 9 13.8129 nan 0.1000 2.2162
## 10 12.1372 nan 0.1000 1.7408
## 20 4.4674 nan 0.1000 0.3276
## 40 2.5738 nan 0.1000 -0.0508
## 60 2.0565 nan 0.1000 -0.0107
## 80 1.7035 nan 0.1000 -0.0131
## 100 1.4354 nan 0.1000 -0.0270
## 120 1.2558 nan 0.1000 -0.0128
## 140 1.0873 nan 0.1000 -0.0285
## 160 0.9588 nan 0.1000 -0.0188
## 180 0.8409 nan 0.1000 -0.0144
## 200 0.7396 nan 0.1000 -0.0125
## 220 0.6429 nan 0.1000 -0.0117
## 240 0.5773 nan 0.1000 -0.0071
## 260 0.5147 nan 0.1000 -0.0098
## 280 0.4687 nan 0.1000 -0.0086
## 300 0.4289 nan 0.1000 -0.0059
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 49.3781 nan 0.1000 10.2082
## 2 41.2664 nan 0.1000 8.5324
## 3 34.9080 nan 0.1000 6.8117
## 4 29.1983 nan 0.1000 6.2143
## 5 25.0246 nan 0.1000 4.3415
## 6 21.2041 nan 0.1000 3.5683
## 7 18.2219 nan 0.1000 2.8348
## 8 15.5300 nan 0.1000 2.5217
## 9 13.6710 nan 0.1000 1.8149
## 10 12.0531 nan 0.1000 1.5602
## 20 4.6794 nan 0.1000 0.2904
## 40 2.8004 nan 0.1000 0.0110
## 60 2.3400 nan 0.1000 -0.0204
## 80 2.0768 nan 0.1000 -0.0229
## 100 1.8216 nan 0.1000 -0.0276
## 120 1.6084 nan 0.1000 -0.0209
## 140 1.4537 nan 0.1000 -0.0200
## 160 1.3244 nan 0.1000 -0.0285
## 180 1.2165 nan 0.1000 -0.0214
## 200 1.1014 nan 0.1000 -0.0251
## 220 0.9935 nan 0.1000 -0.0131
## 240 0.9135 nan 0.1000 -0.0087
## 260 0.8482 nan 0.1000 -0.0224
## 280 0.7740 nan 0.1000 -0.0110
## 300 0.7205 nan 0.1000 -0.0115
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 49.8131 nan 0.1000 10.3738
## 2 41.1052 nan 0.1000 8.3125
## 3 34.7142 nan 0.1000 6.4450
## 4 29.3390 nan 0.1000 5.3870
## 5 25.2650 nan 0.1000 4.5349
## 6 21.6314 nan 0.1000 3.5284
## 7 18.6077 nan 0.1000 2.8149
## 8 16.1288 nan 0.1000 2.3862
## 9 14.0012 nan 0.1000 1.8217
## 10 12.3978 nan 0.1000 1.6263
## 20 5.2632 nan 0.1000 0.2041
## 40 3.2783 nan 0.1000 -0.0134
## 60 2.7730 nan 0.1000 -0.0550
## 80 2.3891 nan 0.1000 -0.0286
## 100 2.1456 nan 0.1000 -0.0409
## 120 2.0076 nan 0.1000 -0.0448
## 140 1.8535 nan 0.1000 -0.0250
## 160 1.7237 nan 0.1000 -0.0103
## 180 1.6003 nan 0.1000 -0.0247
## 200 1.5004 nan 0.1000 -0.0153
## 220 1.3819 nan 0.1000 -0.0124
## 240 1.2903 nan 0.1000 -0.0156
## 260 1.2056 nan 0.1000 -0.0210
## 280 1.1289 nan 0.1000 -0.0183
## 300 1.0644 nan 0.1000 -0.0251
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8622 nan 0.0100 0.7858
## 2 60.1215 nan 0.0100 0.7612
## 3 59.3351 nan 0.0100 0.7847
## 4 58.5539 nan 0.0100 0.7021
## 5 57.7923 nan 0.0100 0.6952
## 6 57.0916 nan 0.0100 0.7035
## 7 56.4124 nan 0.0100 0.6949
## 8 55.7605 nan 0.0100 0.6829
## 9 55.0238 nan 0.0100 0.6909
## 10 54.3772 nan 0.0100 0.6815
## 20 48.1879 nan 0.0100 0.5441
## 40 38.8915 nan 0.0100 0.4001
## 60 31.7702 nan 0.0100 0.2475
## 80 26.2876 nan 0.0100 0.2742
## 100 22.2078 nan 0.0100 0.1748
## 120 18.8923 nan 0.0100 0.1349
## 140 16.3259 nan 0.0100 0.0843
## 160 14.2958 nan 0.0100 0.0954
## 180 12.6315 nan 0.0100 0.0591
## 200 11.2910 nan 0.0100 0.0561
## 220 10.1998 nan 0.0100 0.0392
## 240 9.2695 nan 0.0100 0.0391
## 260 8.4474 nan 0.0100 0.0347
## 280 7.7501 nan 0.0100 0.0161
## 300 7.1513 nan 0.0100 0.0146
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8854 nan 0.0100 0.6969
## 2 60.1073 nan 0.0100 0.7310
## 3 59.3967 nan 0.0100 0.7999
## 4 58.6698 nan 0.0100 0.6938
## 5 57.9319 nan 0.0100 0.7599
## 6 57.2155 nan 0.0100 0.7338
## 7 56.4624 nan 0.0100 0.6651
## 8 55.7420 nan 0.0100 0.6485
## 9 55.0418 nan 0.0100 0.6859
## 10 54.3651 nan 0.0100 0.6285
## 20 48.4772 nan 0.0100 0.4640
## 40 39.0947 nan 0.0100 0.3918
## 60 32.0169 nan 0.0100 0.3185
## 80 26.5058 nan 0.0100 0.2158
## 100 22.3451 nan 0.0100 0.1622
## 120 19.1111 nan 0.0100 0.1380
## 140 16.4672 nan 0.0100 0.1122
## 160 14.4559 nan 0.0100 0.0706
## 180 12.7613 nan 0.0100 0.0576
## 200 11.4189 nan 0.0100 0.0582
## 220 10.2943 nan 0.0100 0.0521
## 240 9.3462 nan 0.0100 0.0483
## 260 8.5165 nan 0.0100 0.0336
## 280 7.8204 nan 0.0100 0.0320
## 300 7.2084 nan 0.0100 0.0127
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.8515 nan 0.0100 0.7710
## 2 60.0740 nan 0.0100 0.7560
## 3 59.3386 nan 0.0100 0.7130
## 4 58.5591 nan 0.0100 0.7299
## 5 57.7918 nan 0.0100 0.7060
## 6 57.0935 nan 0.0100 0.6704
## 7 56.3776 nan 0.0100 0.7286
## 8 55.6486 nan 0.0100 0.7283
## 9 54.9781 nan 0.0100 0.6806
## 10 54.3195 nan 0.0100 0.6347
## 20 48.4234 nan 0.0100 0.5273
## 40 38.8108 nan 0.0100 0.4193
## 60 31.8217 nan 0.0100 0.2951
## 80 26.3999 nan 0.0100 0.2231
## 100 22.2229 nan 0.0100 0.1660
## 120 18.9869 nan 0.0100 0.1055
## 140 16.5039 nan 0.0100 0.0999
## 160 14.4590 nan 0.0100 0.0799
## 180 12.8316 nan 0.0100 0.0526
## 200 11.5245 nan 0.0100 0.0490
## 220 10.3755 nan 0.0100 0.0390
## 240 9.4417 nan 0.0100 0.0358
## 260 8.6588 nan 0.0100 0.0264
## 280 8.0043 nan 0.0100 0.0232
## 300 7.4034 nan 0.0100 0.0221
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6244 nan 0.0100 1.0422
## 2 59.5691 nan 0.0100 1.0235
## 3 58.5444 nan 0.0100 0.9633
## 4 57.5907 nan 0.0100 0.9335
## 5 56.6268 nan 0.0100 0.9290
## 6 55.6790 nan 0.0100 0.8467
## 7 54.7621 nan 0.0100 0.9628
## 8 53.8603 nan 0.0100 0.8882
## 9 53.0019 nan 0.0100 0.7640
## 10 52.1268 nan 0.0100 0.8619
## 20 44.2308 nan 0.0100 0.6923
## 40 32.6187 nan 0.0100 0.5073
## 60 24.4035 nan 0.0100 0.3343
## 80 18.6945 nan 0.0100 0.2406
## 100 14.5752 nan 0.0100 0.1588
## 120 11.6856 nan 0.0100 0.1103
## 140 9.5337 nan 0.0100 0.0979
## 160 7.8776 nan 0.0100 0.0680
## 180 6.6497 nan 0.0100 0.0488
## 200 5.7382 nan 0.0100 0.0406
## 220 5.0562 nan 0.0100 0.0282
## 240 4.5313 nan 0.0100 0.0166
## 260 4.1174 nan 0.0100 0.0002
## 280 3.7969 nan 0.0100 0.0108
## 300 3.5617 nan 0.0100 0.0087
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6497 nan 0.0100 1.0361
## 2 59.6352 nan 0.0100 1.0197
## 3 58.6378 nan 0.0100 1.0019
## 4 57.6418 nan 0.0100 0.9638
## 5 56.6943 nan 0.0100 0.9908
## 6 55.7101 nan 0.0100 0.9005
## 7 54.7836 nan 0.0100 0.8536
## 8 53.9006 nan 0.0100 0.8389
## 9 53.0299 nan 0.0100 0.8221
## 10 52.1675 nan 0.0100 0.8277
## 20 44.4035 nan 0.0100 0.7242
## 40 32.6232 nan 0.0100 0.4740
## 60 24.3441 nan 0.0100 0.3631
## 80 18.5692 nan 0.0100 0.2625
## 100 14.5074 nan 0.0100 0.1387
## 120 11.5852 nan 0.0100 0.1115
## 140 9.4717 nan 0.0100 0.0752
## 160 7.8864 nan 0.0100 0.0677
## 180 6.6630 nan 0.0100 0.0281
## 200 5.7928 nan 0.0100 0.0306
## 220 5.1096 nan 0.0100 0.0206
## 240 4.6103 nan 0.0100 0.0175
## 260 4.2160 nan 0.0100 0.0154
## 280 3.9180 nan 0.0100 0.0018
## 300 3.6937 nan 0.0100 0.0024
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6556 nan 0.0100 1.1317
## 2 59.6703 nan 0.0100 0.9472
## 3 58.7070 nan 0.0100 0.9769
## 4 57.7343 nan 0.0100 0.8783
## 5 56.7474 nan 0.0100 0.9332
## 6 55.8237 nan 0.0100 0.8892
## 7 54.9019 nan 0.0100 0.9539
## 8 54.0377 nan 0.0100 0.7506
## 9 53.1751 nan 0.0100 0.9145
## 10 52.3000 nan 0.0100 0.9598
## 20 44.4790 nan 0.0100 0.7267
## 40 32.7309 nan 0.0100 0.4784
## 60 24.4643 nan 0.0100 0.3220
## 80 18.7168 nan 0.0100 0.2286
## 100 14.7281 nan 0.0100 0.1624
## 120 11.8583 nan 0.0100 0.1210
## 140 9.7632 nan 0.0100 0.0894
## 160 8.1877 nan 0.0100 0.0588
## 180 7.0150 nan 0.0100 0.0585
## 200 6.0665 nan 0.0100 0.0416
## 220 5.3694 nan 0.0100 0.0266
## 240 4.8548 nan 0.0100 0.0183
## 260 4.4771 nan 0.0100 0.0124
## 280 4.1755 nan 0.0100 0.0083
## 300 3.9376 nan 0.0100 0.0031
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5869 nan 0.0100 1.1631
## 2 59.5195 nan 0.0100 0.9420
## 3 58.4932 nan 0.0100 0.9698
## 4 57.4808 nan 0.0100 0.8713
## 5 56.4547 nan 0.0100 1.0556
## 6 55.4551 nan 0.0100 0.8990
## 7 54.4914 nan 0.0100 0.8823
## 8 53.5410 nan 0.0100 0.9121
## 9 52.5978 nan 0.0100 0.8866
## 10 51.7141 nan 0.0100 0.8587
## 20 43.5499 nan 0.0100 0.7624
## 40 31.1845 nan 0.0100 0.5068
## 60 22.7847 nan 0.0100 0.3511
## 80 16.9593 nan 0.0100 0.2376
## 100 12.8032 nan 0.0100 0.1515
## 120 9.8701 nan 0.0100 0.1090
## 140 7.8855 nan 0.0100 0.0831
## 160 6.3999 nan 0.0100 0.0507
## 180 5.3660 nan 0.0100 0.0320
## 200 4.5623 nan 0.0100 0.0250
## 220 3.9722 nan 0.0100 0.0157
## 240 3.5423 nan 0.0100 0.0107
## 260 3.2097 nan 0.0100 0.0054
## 280 2.9834 nan 0.0100 0.0038
## 300 2.7856 nan 0.0100 0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5776 nan 0.0100 1.0296
## 2 59.4933 nan 0.0100 1.0543
## 3 58.4766 nan 0.0100 1.0763
## 4 57.4287 nan 0.0100 1.0608
## 5 56.4451 nan 0.0100 0.9021
## 6 55.4892 nan 0.0100 0.9716
## 7 54.5444 nan 0.0100 1.0167
## 8 53.6095 nan 0.0100 0.9541
## 9 52.6849 nan 0.0100 0.9316
## 10 51.7966 nan 0.0100 0.9580
## 20 43.5624 nan 0.0100 0.7522
## 40 31.1349 nan 0.0100 0.5174
## 60 22.7035 nan 0.0100 0.3389
## 80 16.8254 nan 0.0100 0.2484
## 100 12.8450 nan 0.0100 0.1585
## 120 9.9603 nan 0.0100 0.1072
## 140 7.9663 nan 0.0100 0.0834
## 160 6.5299 nan 0.0100 0.0553
## 180 5.4903 nan 0.0100 0.0396
## 200 4.7390 nan 0.0100 0.0211
## 220 4.1786 nan 0.0100 0.0247
## 240 3.7715 nan 0.0100 0.0042
## 260 3.4738 nan 0.0100 0.0031
## 280 3.2369 nan 0.0100 0.0088
## 300 3.0487 nan 0.0100 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.5996 nan 0.0100 0.8953
## 2 59.5606 nan 0.0100 1.1262
## 3 58.5221 nan 0.0100 0.9993
## 4 57.5063 nan 0.0100 1.0563
## 5 56.5369 nan 0.0100 0.9514
## 6 55.5624 nan 0.0100 1.0539
## 7 54.6471 nan 0.0100 0.9706
## 8 53.7201 nan 0.0100 0.8989
## 9 52.8280 nan 0.0100 0.9081
## 10 51.9032 nan 0.0100 0.8896
## 20 43.6068 nan 0.0100 0.7088
## 40 31.2918 nan 0.0100 0.5292
## 60 22.8699 nan 0.0100 0.3431
## 80 17.1338 nan 0.0100 0.2524
## 100 13.1551 nan 0.0100 0.1440
## 120 10.3012 nan 0.0100 0.1000
## 140 8.3062 nan 0.0100 0.0685
## 160 6.8389 nan 0.0100 0.0501
## 180 5.7844 nan 0.0100 0.0352
## 200 5.0482 nan 0.0100 0.0243
## 220 4.5270 nan 0.0100 0.0179
## 240 4.1464 nan 0.0100 0.0141
## 260 3.8453 nan 0.0100 0.0092
## 280 3.6180 nan 0.0100 0.0048
## 300 3.4441 nan 0.0100 0.0015
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8659 nan 0.0500 3.8502
## 2 54.3191 nan 0.0500 2.9817
## 3 51.1178 nan 0.0500 2.9764
## 4 48.5175 nan 0.0500 2.6572
## 5 45.8940 nan 0.0500 2.7339
## 6 43.4309 nan 0.0500 2.4136
## 7 41.1371 nan 0.0500 2.0524
## 8 38.9164 nan 0.0500 1.9734
## 9 36.7036 nan 0.0500 1.9358
## 10 34.8032 nan 0.0500 1.6942
## 20 22.1542 nan 0.0500 0.8881
## 40 11.3766 nan 0.0500 0.2618
## 60 7.3514 nan 0.0500 0.1510
## 80 5.4349 nan 0.0500 0.0575
## 100 4.4834 nan 0.0500 0.0130
## 120 4.0175 nan 0.0500 -0.0022
## 140 3.7737 nan 0.0500 -0.0117
## 160 3.6121 nan 0.0500 -0.0094
## 180 3.4774 nan 0.0500 -0.0161
## 200 3.3913 nan 0.0500 -0.0053
## 220 3.3333 nan 0.0500 -0.0225
## 240 3.2593 nan 0.0500 -0.0095
## 260 3.2002 nan 0.0500 -0.0098
## 280 3.1515 nan 0.0500 -0.0057
## 300 3.1130 nan 0.0500 -0.0095
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7882 nan 0.0500 3.9965
## 2 54.1363 nan 0.0500 3.3503
## 3 51.1976 nan 0.0500 2.8074
## 4 48.0151 nan 0.0500 2.9378
## 5 45.1334 nan 0.0500 2.6864
## 6 42.6746 nan 0.0500 2.1181
## 7 40.2941 nan 0.0500 2.0977
## 8 38.1431 nan 0.0500 1.9014
## 9 36.2302 nan 0.0500 1.8723
## 10 34.5503 nan 0.0500 1.5922
## 20 21.2147 nan 0.0500 0.8317
## 40 11.1262 nan 0.0500 0.2396
## 60 7.2280 nan 0.0500 0.1520
## 80 5.3339 nan 0.0500 0.0400
## 100 4.4383 nan 0.0500 0.0149
## 120 3.9916 nan 0.0500 0.0022
## 140 3.7708 nan 0.0500 -0.0056
## 160 3.6301 nan 0.0500 -0.0070
## 180 3.5340 nan 0.0500 -0.0038
## 200 3.4543 nan 0.0500 -0.0169
## 220 3.3869 nan 0.0500 -0.0061
## 240 3.3166 nan 0.0500 -0.0064
## 260 3.2699 nan 0.0500 -0.0217
## 280 3.2148 nan 0.0500 -0.0051
## 300 3.1730 nan 0.0500 -0.0054
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.1797 nan 0.0500 3.7733
## 2 54.3168 nan 0.0500 3.4353
## 3 51.3493 nan 0.0500 2.9450
## 4 48.3747 nan 0.0500 2.5359
## 5 45.7498 nan 0.0500 2.9776
## 6 43.3822 nan 0.0500 2.4471
## 7 40.9736 nan 0.0500 2.1355
## 8 38.6822 nan 0.0500 1.9300
## 9 36.8104 nan 0.0500 1.6827
## 10 34.7779 nan 0.0500 1.9006
## 20 22.1031 nan 0.0500 0.8688
## 40 11.2554 nan 0.0500 0.2471
## 60 7.3428 nan 0.0500 0.0969
## 80 5.5532 nan 0.0500 0.0223
## 100 4.7214 nan 0.0500 0.0034
## 120 4.3264 nan 0.0500 0.0089
## 140 4.0786 nan 0.0500 0.0054
## 160 3.9044 nan 0.0500 -0.0118
## 180 3.7743 nan 0.0500 -0.0014
## 200 3.6640 nan 0.0500 -0.0067
## 220 3.5821 nan 0.0500 -0.0008
## 240 3.5075 nan 0.0500 -0.0135
## 260 3.4452 nan 0.0500 -0.0052
## 280 3.3742 nan 0.0500 -0.0028
## 300 3.3140 nan 0.0500 -0.0089
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6147 nan 0.0500 4.6856
## 2 51.9532 nan 0.0500 4.5843
## 3 47.8935 nan 0.0500 4.3724
## 4 44.1853 nan 0.0500 3.8858
## 5 40.7138 nan 0.0500 3.3027
## 6 37.6232 nan 0.0500 2.9641
## 7 34.9742 nan 0.0500 2.8070
## 8 32.3039 nan 0.0500 2.0199
## 9 30.1140 nan 0.0500 2.4929
## 10 27.9446 nan 0.0500 2.1338
## 20 14.7119 nan 0.0500 0.9035
## 40 5.7641 nan 0.0500 0.1531
## 60 3.5447 nan 0.0500 0.0286
## 80 2.8705 nan 0.0500 -0.0125
## 100 2.5672 nan 0.0500 -0.0001
## 120 2.3372 nan 0.0500 -0.0214
## 140 2.1622 nan 0.0500 -0.0159
## 160 2.0329 nan 0.0500 -0.0108
## 180 1.8908 nan 0.0500 -0.0082
## 200 1.7877 nan 0.0500 -0.0048
## 220 1.7013 nan 0.0500 -0.0177
## 240 1.6098 nan 0.0500 -0.0051
## 260 1.5338 nan 0.0500 -0.0086
## 280 1.4620 nan 0.0500 -0.0124
## 300 1.3867 nan 0.0500 -0.0073
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7898 nan 0.0500 4.8951
## 2 52.0735 nan 0.0500 4.4419
## 3 48.0155 nan 0.0500 3.9285
## 4 44.4241 nan 0.0500 4.0645
## 5 40.9792 nan 0.0500 3.2920
## 6 37.9587 nan 0.0500 2.7786
## 7 35.1453 nan 0.0500 2.7625
## 8 32.5770 nan 0.0500 2.7282
## 9 30.1189 nan 0.0500 2.2010
## 10 27.9742 nan 0.0500 2.3167
## 20 14.4494 nan 0.0500 0.9264
## 40 5.7708 nan 0.0500 0.1227
## 60 3.7063 nan 0.0500 0.0164
## 80 3.1016 nan 0.0500 -0.0041
## 100 2.7879 nan 0.0500 -0.0016
## 120 2.5743 nan 0.0500 -0.0170
## 140 2.4015 nan 0.0500 -0.0064
## 160 2.2825 nan 0.0500 -0.0010
## 180 2.1594 nan 0.0500 -0.0077
## 200 2.0689 nan 0.0500 -0.0166
## 220 1.9922 nan 0.0500 -0.0074
## 240 1.9112 nan 0.0500 -0.0172
## 260 1.8217 nan 0.0500 -0.0100
## 280 1.7426 nan 0.0500 -0.0062
## 300 1.6689 nan 0.0500 -0.0057
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8525 nan 0.0500 5.0353
## 2 52.2781 nan 0.0500 4.6157
## 3 48.2328 nan 0.0500 3.8441
## 4 44.6826 nan 0.0500 3.9135
## 5 41.1594 nan 0.0500 3.5338
## 6 38.2077 nan 0.0500 3.0143
## 7 35.3578 nan 0.0500 2.9199
## 8 32.7042 nan 0.0500 2.4874
## 9 30.3569 nan 0.0500 2.2613
## 10 28.2970 nan 0.0500 1.8581
## 20 14.5350 nan 0.0500 0.6874
## 40 6.1512 nan 0.0500 0.1254
## 60 4.0354 nan 0.0500 0.0327
## 80 3.4133 nan 0.0500 0.0098
## 100 3.1163 nan 0.0500 -0.0093
## 120 2.9128 nan 0.0500 -0.0379
## 140 2.7340 nan 0.0500 -0.0071
## 160 2.6080 nan 0.0500 -0.0191
## 180 2.5071 nan 0.0500 -0.0066
## 200 2.4102 nan 0.0500 -0.0176
## 220 2.3238 nan 0.0500 -0.0201
## 240 2.2269 nan 0.0500 -0.0106
## 260 2.1276 nan 0.0500 -0.0070
## 280 2.0660 nan 0.0500 -0.0171
## 300 1.9850 nan 0.0500 -0.0064
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.4497 nan 0.0500 5.2571
## 2 51.4948 nan 0.0500 4.3872
## 3 47.2237 nan 0.0500 4.2788
## 4 43.1012 nan 0.0500 3.9568
## 5 39.6110 nan 0.0500 3.7716
## 6 36.4250 nan 0.0500 3.1385
## 7 33.5711 nan 0.0500 2.8407
## 8 31.0495 nan 0.0500 2.4829
## 9 28.6009 nan 0.0500 2.5751
## 10 26.3251 nan 0.0500 2.1336
## 20 12.4240 nan 0.0500 0.8579
## 40 4.4064 nan 0.0500 0.0845
## 60 2.8496 nan 0.0500 0.0214
## 80 2.3113 nan 0.0500 -0.0207
## 100 1.9595 nan 0.0500 0.0034
## 120 1.7357 nan 0.0500 -0.0081
## 140 1.5718 nan 0.0500 -0.0148
## 160 1.4374 nan 0.0500 -0.0039
## 180 1.3285 nan 0.0500 -0.0044
## 200 1.2149 nan 0.0500 -0.0072
## 220 1.1142 nan 0.0500 -0.0084
## 240 1.0212 nan 0.0500 -0.0084
## 260 0.9448 nan 0.0500 -0.0079
## 280 0.8773 nan 0.0500 -0.0051
## 300 0.8178 nan 0.0500 -0.0071
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1252 nan 0.0500 5.7414
## 2 51.4204 nan 0.0500 4.1735
## 3 46.8492 nan 0.0500 4.3520
## 4 42.9720 nan 0.0500 3.6947
## 5 39.4560 nan 0.0500 3.4321
## 6 36.0655 nan 0.0500 3.3346
## 7 33.0533 nan 0.0500 2.4446
## 8 30.4136 nan 0.0500 2.4410
## 9 27.9294 nan 0.0500 2.0733
## 10 25.8751 nan 0.0500 1.9461
## 20 12.3666 nan 0.0500 0.7241
## 40 4.5891 nan 0.0500 0.1474
## 60 3.0616 nan 0.0500 0.0053
## 80 2.5462 nan 0.0500 -0.0135
## 100 2.2825 nan 0.0500 0.0018
## 120 2.0888 nan 0.0500 -0.0110
## 140 1.9180 nan 0.0500 -0.0089
## 160 1.7699 nan 0.0500 -0.0045
## 180 1.6561 nan 0.0500 -0.0124
## 200 1.5505 nan 0.0500 -0.0150
## 220 1.4600 nan 0.0500 -0.0164
## 240 1.3829 nan 0.0500 -0.0084
## 260 1.3043 nan 0.0500 -0.0112
## 280 1.2221 nan 0.0500 -0.0104
## 300 1.1498 nan 0.0500 -0.0104
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.5323 nan 0.0500 4.4028
## 2 51.6696 nan 0.0500 4.9683
## 3 47.4301 nan 0.0500 4.4332
## 4 43.4831 nan 0.0500 3.5643
## 5 39.9808 nan 0.0500 3.6605
## 6 36.7691 nan 0.0500 2.8788
## 7 33.8249 nan 0.0500 2.6688
## 8 31.2318 nan 0.0500 2.5521
## 9 28.7939 nan 0.0500 2.2137
## 10 26.6234 nan 0.0500 2.2237
## 20 12.8953 nan 0.0500 0.9425
## 40 5.0654 nan 0.0500 0.1479
## 60 3.4858 nan 0.0500 -0.0025
## 80 2.9627 nan 0.0500 -0.0176
## 100 2.6740 nan 0.0500 -0.0081
## 120 2.4513 nan 0.0500 -0.0176
## 140 2.2664 nan 0.0500 -0.0130
## 160 2.1224 nan 0.0500 -0.0156
## 180 1.9930 nan 0.0500 -0.0065
## 200 1.8956 nan 0.0500 -0.0073
## 220 1.8014 nan 0.0500 -0.0104
## 240 1.7221 nan 0.0500 -0.0064
## 260 1.6321 nan 0.0500 -0.0195
## 280 1.5648 nan 0.0500 -0.0026
## 300 1.5015 nan 0.0500 -0.0089
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.9441 nan 0.1000 7.2420
## 2 47.8271 nan 0.1000 6.1404
## 3 42.9778 nan 0.1000 5.0376
## 4 38.4820 nan 0.1000 4.3274
## 5 34.5962 nan 0.1000 3.3048
## 6 31.2972 nan 0.1000 3.4166
## 7 28.1830 nan 0.1000 2.7391
## 8 26.0307 nan 0.1000 2.2424
## 9 23.8398 nan 0.1000 1.9182
## 10 21.7298 nan 0.1000 1.9186
## 20 10.8547 nan 0.1000 0.4527
## 40 5.1427 nan 0.1000 0.0812
## 60 3.8430 nan 0.1000 -0.0041
## 80 3.4858 nan 0.1000 -0.0395
## 100 3.2962 nan 0.1000 -0.0252
## 120 3.1812 nan 0.1000 -0.0162
## 140 3.1310 nan 0.1000 -0.0203
## 160 3.0434 nan 0.1000 -0.0263
## 180 2.9638 nan 0.1000 -0.0199
## 200 2.8911 nan 0.1000 -0.0234
## 220 2.8315 nan 0.1000 -0.0251
## 240 2.7713 nan 0.1000 -0.0103
## 260 2.7231 nan 0.1000 -0.0212
## 280 2.6790 nan 0.1000 -0.0173
## 300 2.6449 nan 0.1000 -0.0278
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.2811 nan 0.1000 7.7156
## 2 48.5306 nan 0.1000 5.1232
## 3 43.1711 nan 0.1000 5.3144
## 4 38.7685 nan 0.1000 4.2570
## 5 35.3232 nan 0.1000 3.6249
## 6 31.6729 nan 0.1000 3.3728
## 7 28.6366 nan 0.1000 2.9479
## 8 26.3530 nan 0.1000 2.1285
## 9 24.2800 nan 0.1000 2.1439
## 10 22.1598 nan 0.1000 1.8364
## 20 11.3253 nan 0.1000 0.7188
## 40 5.3606 nan 0.1000 0.0523
## 60 4.0782 nan 0.1000 0.0203
## 80 3.7381 nan 0.1000 -0.0153
## 100 3.5337 nan 0.1000 0.0049
## 120 3.3590 nan 0.1000 -0.0180
## 140 3.2575 nan 0.1000 -0.0073
## 160 3.1802 nan 0.1000 -0.0311
## 180 3.0985 nan 0.1000 -0.0099
## 200 3.0269 nan 0.1000 -0.0199
## 220 2.9687 nan 0.1000 -0.0187
## 240 2.9297 nan 0.1000 -0.0084
## 260 2.8581 nan 0.1000 -0.0240
## 280 2.8179 nan 0.1000 -0.0120
## 300 2.7729 nan 0.1000 -0.0103
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.4760 nan 0.1000 7.6332
## 2 47.5863 nan 0.1000 5.2700
## 3 42.2104 nan 0.1000 5.3414
## 4 37.7294 nan 0.1000 4.1433
## 5 33.6294 nan 0.1000 3.6448
## 6 30.1534 nan 0.1000 3.0533
## 7 27.2197 nan 0.1000 2.7926
## 8 24.8262 nan 0.1000 1.9462
## 9 22.6172 nan 0.1000 2.1359
## 10 20.7690 nan 0.1000 1.9686
## 20 10.7421 nan 0.1000 0.4988
## 40 5.2405 nan 0.1000 0.0935
## 60 4.0977 nan 0.1000 -0.0076
## 80 3.7719 nan 0.1000 0.0003
## 100 3.5611 nan 0.1000 -0.0232
## 120 3.4025 nan 0.1000 -0.0198
## 140 3.2895 nan 0.1000 -0.0189
## 160 3.2133 nan 0.1000 -0.0145
## 180 3.1242 nan 0.1000 -0.0129
## 200 3.0728 nan 0.1000 -0.0185
## 220 2.9869 nan 0.1000 -0.0152
## 240 2.9350 nan 0.1000 -0.0125
## 260 2.8926 nan 0.1000 -0.0142
## 280 2.8366 nan 0.1000 -0.0054
## 300 2.8009 nan 0.1000 -0.0111
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.2862 nan 0.1000 10.3712
## 2 43.4136 nan 0.1000 6.2938
## 3 36.8516 nan 0.1000 6.5241
## 4 31.3711 nan 0.1000 5.7309
## 5 27.3633 nan 0.1000 4.0482
## 6 23.4733 nan 0.1000 3.8651
## 7 20.2912 nan 0.1000 3.2508
## 8 17.7051 nan 0.1000 2.4428
## 9 15.4944 nan 0.1000 1.9867
## 10 13.5572 nan 0.1000 1.8592
## 20 5.5870 nan 0.1000 0.3227
## 40 2.9582 nan 0.1000 -0.0131
## 60 2.4322 nan 0.1000 0.0033
## 80 2.1164 nan 0.1000 -0.0086
## 100 1.8792 nan 0.1000 -0.0453
## 120 1.7086 nan 0.1000 -0.0026
## 140 1.5568 nan 0.1000 -0.0170
## 160 1.4502 nan 0.1000 -0.0178
## 180 1.3317 nan 0.1000 -0.0101
## 200 1.2255 nan 0.1000 -0.0256
## 220 1.1453 nan 0.1000 -0.0156
## 240 1.0606 nan 0.1000 -0.0133
## 260 0.9894 nan 0.1000 -0.0109
## 280 0.9172 nan 0.1000 -0.0093
## 300 0.8677 nan 0.1000 -0.0068
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7152 nan 0.1000 9.1557
## 2 43.8506 nan 0.1000 6.9289
## 3 37.0778 nan 0.1000 5.8324
## 4 31.7620 nan 0.1000 5.4133
## 5 27.2661 nan 0.1000 3.8028
## 6 23.4308 nan 0.1000 3.3356
## 7 20.2442 nan 0.1000 2.9293
## 8 17.7938 nan 0.1000 2.2916
## 9 15.8277 nan 0.1000 2.0609
## 10 13.9468 nan 0.1000 1.6470
## 20 5.6296 nan 0.1000 0.3829
## 40 3.1728 nan 0.1000 -0.0289
## 60 2.6891 nan 0.1000 -0.0633
## 80 2.3955 nan 0.1000 -0.0499
## 100 2.2032 nan 0.1000 -0.0224
## 120 2.0237 nan 0.1000 -0.0349
## 140 1.8670 nan 0.1000 -0.0168
## 160 1.7399 nan 0.1000 -0.0255
## 180 1.6253 nan 0.1000 -0.0057
## 200 1.4989 nan 0.1000 -0.0107
## 220 1.3953 nan 0.1000 -0.0080
## 240 1.3051 nan 0.1000 -0.0089
## 260 1.2243 nan 0.1000 -0.0156
## 280 1.1630 nan 0.1000 -0.0087
## 300 1.1043 nan 0.1000 -0.0125
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.4981 nan 0.1000 8.8722
## 2 43.6963 nan 0.1000 6.9849
## 3 37.1869 nan 0.1000 6.6393
## 4 31.9387 nan 0.1000 5.5176
## 5 27.3425 nan 0.1000 4.1106
## 6 23.4002 nan 0.1000 3.9126
## 7 20.4652 nan 0.1000 2.9522
## 8 17.9750 nan 0.1000 2.4698
## 9 15.8197 nan 0.1000 1.8620
## 10 13.8155 nan 0.1000 1.6188
## 20 5.8215 nan 0.1000 0.3007
## 40 3.4309 nan 0.1000 -0.0030
## 60 2.9204 nan 0.1000 0.0017
## 80 2.5932 nan 0.1000 -0.0239
## 100 2.3570 nan 0.1000 -0.0199
## 120 2.1887 nan 0.1000 -0.0062
## 140 2.0648 nan 0.1000 -0.0162
## 160 1.9457 nan 0.1000 -0.0180
## 180 1.8223 nan 0.1000 -0.0215
## 200 1.7148 nan 0.1000 -0.0220
## 220 1.6322 nan 0.1000 -0.0170
## 240 1.5625 nan 0.1000 -0.0116
## 260 1.5047 nan 0.1000 -0.0212
## 280 1.4416 nan 0.1000 -0.0127
## 300 1.3683 nan 0.1000 -0.0134
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.1744 nan 0.1000 9.3356
## 2 42.8662 nan 0.1000 7.7694
## 3 35.9292 nan 0.1000 6.2969
## 4 30.1164 nan 0.1000 5.0818
## 5 25.7409 nan 0.1000 4.2125
## 6 21.7417 nan 0.1000 3.8264
## 7 18.6213 nan 0.1000 3.0381
## 8 15.6919 nan 0.1000 2.4127
## 9 13.3818 nan 0.1000 2.0423
## 10 11.6152 nan 0.1000 1.2527
## 20 4.3874 nan 0.1000 0.2272
## 40 2.3717 nan 0.1000 0.0035
## 60 1.8397 nan 0.1000 0.0016
## 80 1.5327 nan 0.1000 -0.0209
## 100 1.3159 nan 0.1000 -0.0375
## 120 1.1107 nan 0.1000 -0.0204
## 140 0.9601 nan 0.1000 -0.0145
## 160 0.8381 nan 0.1000 -0.0111
## 180 0.7415 nan 0.1000 -0.0050
## 200 0.6485 nan 0.1000 -0.0091
## 220 0.5703 nan 0.1000 -0.0078
## 240 0.5192 nan 0.1000 -0.0112
## 260 0.4702 nan 0.1000 -0.0083
## 280 0.4251 nan 0.1000 -0.0114
## 300 0.3761 nan 0.1000 -0.0072
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.4627 nan 0.1000 10.8696
## 2 43.1587 nan 0.1000 8.7122
## 3 35.9611 nan 0.1000 6.8755
## 4 30.4888 nan 0.1000 4.8808
## 5 25.9710 nan 0.1000 4.9787
## 6 22.2309 nan 0.1000 3.3146
## 7 19.1096 nan 0.1000 3.0082
## 8 16.5596 nan 0.1000 2.3615
## 9 14.2646 nan 0.1000 1.8845
## 10 12.4170 nan 0.1000 1.8401
## 20 4.5808 nan 0.1000 0.2301
## 40 2.6547 nan 0.1000 -0.0467
## 60 2.2096 nan 0.1000 -0.0087
## 80 1.8899 nan 0.1000 -0.0253
## 100 1.6260 nan 0.1000 -0.0434
## 120 1.4313 nan 0.1000 -0.0183
## 140 1.2741 nan 0.1000 -0.0183
## 160 1.1372 nan 0.1000 -0.0096
## 180 1.0120 nan 0.1000 -0.0131
## 200 0.9245 nan 0.1000 -0.0136
## 220 0.8249 nan 0.1000 -0.0091
## 240 0.7598 nan 0.1000 -0.0113
## 260 0.6955 nan 0.1000 -0.0096
## 280 0.6406 nan 0.1000 -0.0045
## 300 0.5900 nan 0.1000 -0.0158
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.5008 nan 0.1000 10.6072
## 2 43.2465 nan 0.1000 8.1726
## 3 35.9253 nan 0.1000 7.0885
## 4 30.6692 nan 0.1000 5.1548
## 5 25.9915 nan 0.1000 4.4196
## 6 22.3171 nan 0.1000 3.6442
## 7 19.2065 nan 0.1000 2.9365
## 8 16.4438 nan 0.1000 2.4022
## 9 14.6115 nan 0.1000 2.1859
## 10 12.7389 nan 0.1000 1.6453
## 20 5.0581 nan 0.1000 0.2565
## 40 3.0549 nan 0.1000 -0.0185
## 60 2.5366 nan 0.1000 -0.0198
## 80 2.1868 nan 0.1000 -0.0116
## 100 1.9244 nan 0.1000 -0.0173
## 120 1.7636 nan 0.1000 -0.0295
## 140 1.6170 nan 0.1000 -0.0245
## 160 1.4767 nan 0.1000 -0.0115
## 180 1.3540 nan 0.1000 -0.0082
## 200 1.2397 nan 0.1000 -0.0068
## 220 1.1560 nan 0.1000 -0.0339
## 240 1.0796 nan 0.1000 -0.0165
## 260 1.0065 nan 0.1000 -0.0076
## 280 0.9416 nan 0.1000 -0.0055
## 300 0.8828 nan 0.1000 -0.0092
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.2879 nan 0.0100 0.7918
## 2 61.5685 nan 0.0100 0.7657
## 3 60.7858 nan 0.0100 0.7480
## 4 60.0537 nan 0.0100 0.6681
## 5 59.2752 nan 0.0100 0.7176
## 6 58.5217 nan 0.0100 0.7559
## 7 57.7783 nan 0.0100 0.6767
## 8 57.0283 nan 0.0100 0.7019
## 9 56.3555 nan 0.0100 0.6650
## 10 55.6776 nan 0.0100 0.6584
## 20 49.7642 nan 0.0100 0.5350
## 40 40.1004 nan 0.0100 0.3794
## 60 32.7544 nan 0.0100 0.2946
## 80 27.3242 nan 0.0100 0.2446
## 100 23.0147 nan 0.0100 0.1844
## 120 19.5939 nan 0.0100 0.1319
## 140 16.9658 nan 0.0100 0.1027
## 160 14.8136 nan 0.0100 0.0811
## 180 13.1525 nan 0.0100 0.0689
## 200 11.6936 nan 0.0100 0.0564
## 220 10.5508 nan 0.0100 0.0321
## 240 9.5690 nan 0.0100 0.0397
## 260 8.7437 nan 0.0100 0.0307
## 280 8.0567 nan 0.0100 0.0194
## 300 7.4696 nan 0.0100 0.0115
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.2548 nan 0.0100 0.8061
## 2 61.4699 nan 0.0100 0.7915
## 3 60.7402 nan 0.0100 0.7577
## 4 59.8992 nan 0.0100 0.7334
## 5 59.1060 nan 0.0100 0.7529
## 6 58.3326 nan 0.0100 0.7964
## 7 57.6439 nan 0.0100 0.6765
## 8 56.8926 nan 0.0100 0.7111
## 9 56.2014 nan 0.0100 0.5897
## 10 55.4997 nan 0.0100 0.6717
## 20 49.5355 nan 0.0100 0.5851
## 40 40.0924 nan 0.0100 0.3333
## 60 32.7896 nan 0.0100 0.2942
## 80 27.3288 nan 0.0100 0.2141
## 100 23.0735 nan 0.0100 0.1567
## 120 19.6550 nan 0.0100 0.1192
## 140 16.8807 nan 0.0100 0.0819
## 160 14.7496 nan 0.0100 0.0874
## 180 13.0081 nan 0.0100 0.0522
## 200 11.6161 nan 0.0100 0.0438
## 220 10.4591 nan 0.0100 0.0441
## 240 9.4867 nan 0.0100 0.0511
## 260 8.6952 nan 0.0100 0.0252
## 280 7.9925 nan 0.0100 0.0242
## 300 7.3941 nan 0.0100 0.0219
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.2376 nan 0.0100 0.7897
## 2 61.4274 nan 0.0100 0.7131
## 3 60.6555 nan 0.0100 0.7738
## 4 59.9434 nan 0.0100 0.6974
## 5 59.3473 nan 0.0100 0.5464
## 6 58.6124 nan 0.0100 0.7491
## 7 57.8910 nan 0.0100 0.7126
## 8 57.1892 nan 0.0100 0.7504
## 9 56.4092 nan 0.0100 0.7313
## 10 55.7567 nan 0.0100 0.6811
## 20 49.6541 nan 0.0100 0.5336
## 40 40.1147 nan 0.0100 0.3611
## 60 32.7073 nan 0.0100 0.3111
## 80 27.1754 nan 0.0100 0.2129
## 100 22.8383 nan 0.0100 0.1725
## 120 19.5164 nan 0.0100 0.1147
## 140 16.8971 nan 0.0100 0.1019
## 160 14.7879 nan 0.0100 0.0870
## 180 13.0736 nan 0.0100 0.0699
## 200 11.6870 nan 0.0100 0.0612
## 220 10.5845 nan 0.0100 0.0392
## 240 9.6453 nan 0.0100 0.0359
## 260 8.8397 nan 0.0100 0.0334
## 280 8.1239 nan 0.0100 0.0272
## 300 7.5593 nan 0.0100 0.0132
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9539 nan 0.0100 1.1668
## 2 60.9824 nan 0.0100 0.9683
## 3 59.9876 nan 0.0100 0.8909
## 4 59.0259 nan 0.0100 0.9880
## 5 58.0166 nan 0.0100 1.0308
## 6 57.0378 nan 0.0100 0.9104
## 7 56.0812 nan 0.0100 0.9204
## 8 55.1884 nan 0.0100 0.8286
## 9 54.3192 nan 0.0100 0.8596
## 10 53.4327 nan 0.0100 0.8219
## 20 45.3861 nan 0.0100 0.6761
## 40 33.2536 nan 0.0100 0.5033
## 60 24.8681 nan 0.0100 0.3638
## 80 18.9626 nan 0.0100 0.2326
## 100 14.7304 nan 0.0100 0.1699
## 120 11.7940 nan 0.0100 0.1113
## 140 9.6475 nan 0.0100 0.0868
## 160 7.9975 nan 0.0100 0.0668
## 180 6.7912 nan 0.0100 0.0385
## 200 5.8523 nan 0.0100 0.0371
## 220 5.1535 nan 0.0100 0.0173
## 240 4.6072 nan 0.0100 0.0190
## 260 4.1991 nan 0.0100 0.0108
## 280 3.8961 nan 0.0100 0.0095
## 300 3.6450 nan 0.0100 0.0024
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0046 nan 0.0100 1.0918
## 2 61.0094 nan 0.0100 0.9332
## 3 59.9559 nan 0.0100 0.9915
## 4 58.9444 nan 0.0100 1.0314
## 5 57.9191 nan 0.0100 1.0016
## 6 56.9410 nan 0.0100 0.9067
## 7 55.9738 nan 0.0100 0.9345
## 8 55.0426 nan 0.0100 0.8482
## 9 54.1755 nan 0.0100 0.8576
## 10 53.2549 nan 0.0100 0.8931
## 20 45.3461 nan 0.0100 0.6892
## 40 33.2212 nan 0.0100 0.4885
## 60 24.7630 nan 0.0100 0.3688
## 80 18.8274 nan 0.0100 0.2268
## 100 14.6583 nan 0.0100 0.1837
## 120 11.7113 nan 0.0100 0.1153
## 140 9.5935 nan 0.0100 0.0802
## 160 7.9960 nan 0.0100 0.0660
## 180 6.8114 nan 0.0100 0.0391
## 200 5.9353 nan 0.0100 0.0322
## 220 5.2488 nan 0.0100 0.0202
## 240 4.7346 nan 0.0100 0.0123
## 260 4.3508 nan 0.0100 0.0105
## 280 4.0461 nan 0.0100 0.0071
## 300 3.8115 nan 0.0100 0.0040
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.0001 nan 0.0100 1.0740
## 2 60.9038 nan 0.0100 1.1083
## 3 59.9266 nan 0.0100 0.9264
## 4 59.0104 nan 0.0100 0.9228
## 5 58.0411 nan 0.0100 0.9830
## 6 57.0756 nan 0.0100 1.0119
## 7 56.0865 nan 0.0100 0.8535
## 8 55.1521 nan 0.0100 1.0303
## 9 54.2604 nan 0.0100 0.8890
## 10 53.3742 nan 0.0100 0.8876
## 20 45.3899 nan 0.0100 0.7436
## 40 33.4799 nan 0.0100 0.5031
## 60 24.9248 nan 0.0100 0.3397
## 80 18.9729 nan 0.0100 0.2201
## 100 14.8811 nan 0.0100 0.1648
## 120 11.9465 nan 0.0100 0.1118
## 140 9.8061 nan 0.0100 0.0858
## 160 8.1776 nan 0.0100 0.0678
## 180 6.9984 nan 0.0100 0.0412
## 200 6.1040 nan 0.0100 0.0229
## 220 5.4288 nan 0.0100 0.0285
## 240 4.9209 nan 0.0100 0.0114
## 260 4.5227 nan 0.0100 0.0147
## 280 4.2334 nan 0.0100 0.0076
## 300 4.0007 nan 0.0100 0.0035
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9837 nan 0.0100 1.2660
## 2 60.8948 nan 0.0100 1.0841
## 3 59.8178 nan 0.0100 1.0196
## 4 58.7476 nan 0.0100 1.0885
## 5 57.7419 nan 0.0100 0.9799
## 6 56.6968 nan 0.0100 0.9575
## 7 55.6822 nan 0.0100 0.9727
## 8 54.7111 nan 0.0100 0.8663
## 9 53.7506 nan 0.0100 1.0013
## 10 52.8013 nan 0.0100 0.8626
## 20 44.3519 nan 0.0100 0.7802
## 40 31.7695 nan 0.0100 0.4407
## 60 23.1116 nan 0.0100 0.3589
## 80 17.1328 nan 0.0100 0.2116
## 100 12.9814 nan 0.0100 0.1636
## 120 10.0087 nan 0.0100 0.1206
## 140 7.9274 nan 0.0100 0.0700
## 160 6.4625 nan 0.0100 0.0539
## 180 5.4034 nan 0.0100 0.0401
## 200 4.6191 nan 0.0100 0.0308
## 220 4.0379 nan 0.0100 0.0180
## 240 3.6145 nan 0.0100 0.0107
## 260 3.2971 nan 0.0100 0.0077
## 280 3.0615 nan 0.0100 0.0058
## 300 2.8623 nan 0.0100 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9774 nan 0.0100 1.0771
## 2 60.8538 nan 0.0100 0.9865
## 3 59.7732 nan 0.0100 1.1052
## 4 58.7242 nan 0.0100 0.9586
## 5 57.7486 nan 0.0100 0.9731
## 6 56.7873 nan 0.0100 0.9770
## 7 55.8306 nan 0.0100 0.9614
## 8 54.8646 nan 0.0100 0.9977
## 9 53.9152 nan 0.0100 1.0610
## 10 52.9340 nan 0.0100 0.8258
## 20 44.5457 nan 0.0100 0.7060
## 40 32.0156 nan 0.0100 0.4970
## 60 23.2883 nan 0.0100 0.3717
## 80 17.2602 nan 0.0100 0.2312
## 100 12.9955 nan 0.0100 0.1558
## 120 10.0678 nan 0.0100 0.0866
## 140 8.0146 nan 0.0100 0.0738
## 160 6.5738 nan 0.0100 0.0539
## 180 5.5549 nan 0.0100 0.0431
## 200 4.7982 nan 0.0100 0.0277
## 220 4.2543 nan 0.0100 0.0086
## 240 3.8395 nan 0.0100 0.0132
## 260 3.5265 nan 0.0100 0.0030
## 280 3.2940 nan 0.0100 0.0060
## 300 3.0925 nan 0.0100 0.0046
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.9154 nan 0.0100 1.1238
## 2 60.8149 nan 0.0100 1.1661
## 3 59.7617 nan 0.0100 1.1066
## 4 58.7571 nan 0.0100 0.9320
## 5 57.7178 nan 0.0100 0.9879
## 6 56.7076 nan 0.0100 1.0182
## 7 55.7295 nan 0.0100 0.8906
## 8 54.7830 nan 0.0100 0.9281
## 9 53.7979 nan 0.0100 0.9521
## 10 52.8848 nan 0.0100 0.9026
## 20 44.3568 nan 0.0100 0.7371
## 40 31.8351 nan 0.0100 0.5580
## 60 23.2002 nan 0.0100 0.2975
## 80 17.3519 nan 0.0100 0.2460
## 100 13.2818 nan 0.0100 0.1677
## 120 10.3823 nan 0.0100 0.1282
## 140 8.3679 nan 0.0100 0.0746
## 160 6.9204 nan 0.0100 0.0556
## 180 5.8913 nan 0.0100 0.0357
## 200 5.1662 nan 0.0100 0.0266
## 220 4.6272 nan 0.0100 0.0134
## 240 4.2216 nan 0.0100 0.0055
## 260 3.9407 nan 0.0100 0.0066
## 280 3.7110 nan 0.0100 0.0042
## 300 3.5175 nan 0.0100 0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3956 nan 0.0500 3.8244
## 2 55.6653 nan 0.0500 3.5007
## 3 52.5359 nan 0.0500 3.4368
## 4 49.7533 nan 0.0500 2.9200
## 5 46.9020 nan 0.0500 2.5685
## 6 44.4170 nan 0.0500 2.2151
## 7 41.7859 nan 0.0500 2.7660
## 8 39.8121 nan 0.0500 1.8870
## 9 37.6411 nan 0.0500 1.9385
## 10 35.6523 nan 0.0500 1.9580
## 20 22.8008 nan 0.0500 0.9617
## 40 11.8900 nan 0.0500 0.3024
## 60 7.5376 nan 0.0500 0.1444
## 80 5.5703 nan 0.0500 0.0462
## 100 4.5750 nan 0.0500 0.0356
## 120 4.0844 nan 0.0500 -0.0111
## 140 3.8096 nan 0.0500 -0.0011
## 160 3.6646 nan 0.0500 -0.0071
## 180 3.5481 nan 0.0500 -0.0235
## 200 3.4625 nan 0.0500 -0.0069
## 220 3.3860 nan 0.0500 -0.0044
## 240 3.3084 nan 0.0500 -0.0041
## 260 3.2543 nan 0.0500 -0.0039
## 280 3.2087 nan 0.0500 -0.0103
## 300 3.1608 nan 0.0500 -0.0035
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.3533 nan 0.0500 4.0646
## 2 55.7381 nan 0.0500 3.6016
## 3 52.2986 nan 0.0500 2.9537
## 4 49.0737 nan 0.0500 2.8889
## 5 46.4285 nan 0.0500 2.6391
## 6 43.8298 nan 0.0500 2.2559
## 7 41.4434 nan 0.0500 2.0393
## 8 39.3361 nan 0.0500 1.8964
## 9 37.4128 nan 0.0500 1.8635
## 10 35.5320 nan 0.0500 1.5653
## 20 22.3762 nan 0.0500 0.6353
## 40 11.4130 nan 0.0500 0.2955
## 60 7.3123 nan 0.0500 0.1036
## 80 5.5370 nan 0.0500 0.0393
## 100 4.5894 nan 0.0500 0.0257
## 120 4.1180 nan 0.0500 -0.0089
## 140 3.8594 nan 0.0500 -0.0062
## 160 3.7206 nan 0.0500 -0.0059
## 180 3.6166 nan 0.0500 -0.0042
## 200 3.5120 nan 0.0500 -0.0006
## 220 3.4236 nan 0.0500 -0.0028
## 240 3.3525 nan 0.0500 -0.0119
## 260 3.2847 nan 0.0500 -0.0046
## 280 3.2129 nan 0.0500 -0.0060
## 300 3.1649 nan 0.0500 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.1588 nan 0.0500 3.8371
## 2 55.3994 nan 0.0500 3.4684
## 3 52.2227 nan 0.0500 2.8593
## 4 49.1109 nan 0.0500 2.8627
## 5 46.3233 nan 0.0500 2.5798
## 6 44.1209 nan 0.0500 2.1129
## 7 41.6351 nan 0.0500 2.3346
## 8 39.7039 nan 0.0500 2.0924
## 9 37.3696 nan 0.0500 1.8478
## 10 35.4331 nan 0.0500 1.4631
## 20 22.3967 nan 0.0500 0.9002
## 40 11.4412 nan 0.0500 0.3380
## 60 7.4127 nan 0.0500 0.1385
## 80 5.6496 nan 0.0500 0.0358
## 100 4.8217 nan 0.0500 0.0150
## 120 4.4031 nan 0.0500 0.0026
## 140 4.1762 nan 0.0500 0.0078
## 160 4.0230 nan 0.0500 0.0039
## 180 3.9136 nan 0.0500 -0.0075
## 200 3.7983 nan 0.0500 -0.0005
## 220 3.7045 nan 0.0500 -0.0007
## 240 3.6205 nan 0.0500 0.0013
## 260 3.5399 nan 0.0500 -0.0111
## 280 3.4428 nan 0.0500 -0.0072
## 300 3.3816 nan 0.0500 0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8096 nan 0.0500 4.5339
## 2 53.2912 nan 0.0500 4.8666
## 3 49.0263 nan 0.0500 4.0866
## 4 45.0731 nan 0.0500 3.6521
## 5 41.7066 nan 0.0500 3.4011
## 6 38.5323 nan 0.0500 2.9258
## 7 35.5658 nan 0.0500 2.9457
## 8 32.8483 nan 0.0500 2.6071
## 9 30.4466 nan 0.0500 2.4743
## 10 28.1842 nan 0.0500 2.3287
## 20 14.4072 nan 0.0500 0.8618
## 40 5.6984 nan 0.0500 0.1395
## 60 3.6580 nan 0.0500 0.0217
## 80 2.9315 nan 0.0500 0.0156
## 100 2.5774 nan 0.0500 0.0003
## 120 2.3260 nan 0.0500 -0.0031
## 140 2.1214 nan 0.0500 -0.0151
## 160 1.9488 nan 0.0500 -0.0079
## 180 1.8195 nan 0.0500 -0.0107
## 200 1.7380 nan 0.0500 -0.0136
## 220 1.6520 nan 0.0500 -0.0037
## 240 1.5563 nan 0.0500 -0.0117
## 260 1.4752 nan 0.0500 -0.0059
## 280 1.3987 nan 0.0500 -0.0109
## 300 1.3375 nan 0.0500 -0.0120
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0403 nan 0.0500 4.9046
## 2 53.6307 nan 0.0500 4.7823
## 3 49.2229 nan 0.0500 4.6157
## 4 45.3146 nan 0.0500 3.9865
## 5 41.5877 nan 0.0500 3.4740
## 6 38.4794 nan 0.0500 3.3244
## 7 35.5854 nan 0.0500 2.7736
## 8 32.7445 nan 0.0500 2.3901
## 9 30.5538 nan 0.0500 2.2682
## 10 28.3767 nan 0.0500 2.3250
## 20 14.6102 nan 0.0500 0.8099
## 40 5.9244 nan 0.0500 0.1538
## 60 3.8559 nan 0.0500 0.0277
## 80 3.1458 nan 0.0500 -0.0006
## 100 2.7893 nan 0.0500 0.0020
## 120 2.5468 nan 0.0500 -0.0062
## 140 2.3860 nan 0.0500 -0.0029
## 160 2.2235 nan 0.0500 -0.0117
## 180 2.0948 nan 0.0500 -0.0013
## 200 1.9998 nan 0.0500 -0.0088
## 220 1.9197 nan 0.0500 -0.0115
## 240 1.8368 nan 0.0500 -0.0134
## 260 1.7651 nan 0.0500 -0.0107
## 280 1.7017 nan 0.0500 -0.0059
## 300 1.6435 nan 0.0500 -0.0104
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7374 nan 0.0500 6.0841
## 2 53.0715 nan 0.0500 4.4158
## 3 48.9888 nan 0.0500 4.3379
## 4 45.1731 nan 0.0500 3.8862
## 5 41.6098 nan 0.0500 3.7575
## 6 38.2443 nan 0.0500 2.9238
## 7 35.5846 nan 0.0500 2.6468
## 8 32.8897 nan 0.0500 2.6699
## 9 30.4454 nan 0.0500 2.4746
## 10 28.2602 nan 0.0500 2.1774
## 20 14.7010 nan 0.0500 0.8192
## 40 6.0886 nan 0.0500 0.1375
## 60 4.0019 nan 0.0500 0.0443
## 80 3.3635 nan 0.0500 -0.0001
## 100 3.0289 nan 0.0500 -0.0185
## 120 2.7856 nan 0.0500 -0.0129
## 140 2.5755 nan 0.0500 -0.0099
## 160 2.4370 nan 0.0500 -0.0085
## 180 2.3030 nan 0.0500 -0.0055
## 200 2.1950 nan 0.0500 -0.0146
## 220 2.0819 nan 0.0500 -0.0065
## 240 1.9920 nan 0.0500 -0.0106
## 260 1.9206 nan 0.0500 -0.0125
## 280 1.8515 nan 0.0500 -0.0042
## 300 1.7872 nan 0.0500 -0.0075
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.3901 nan 0.0500 5.9261
## 2 52.7396 nan 0.0500 4.9300
## 3 48.0968 nan 0.0500 4.5091
## 4 44.0206 nan 0.0500 3.8374
## 5 40.3544 nan 0.0500 3.9691
## 6 36.8693 nan 0.0500 3.4354
## 7 33.7800 nan 0.0500 3.0243
## 8 30.9552 nan 0.0500 2.6098
## 9 28.5795 nan 0.0500 2.3263
## 10 26.3316 nan 0.0500 2.1134
## 20 12.5268 nan 0.0500 0.8300
## 40 4.5924 nan 0.0500 0.1748
## 60 2.9367 nan 0.0500 -0.0016
## 80 2.3302 nan 0.0500 -0.0138
## 100 2.0168 nan 0.0500 -0.0226
## 120 1.7391 nan 0.0500 -0.0108
## 140 1.5756 nan 0.0500 -0.0085
## 160 1.4201 nan 0.0500 -0.0081
## 180 1.2799 nan 0.0500 -0.0068
## 200 1.1660 nan 0.0500 -0.0067
## 220 1.0653 nan 0.0500 -0.0126
## 240 0.9907 nan 0.0500 -0.0103
## 260 0.9093 nan 0.0500 -0.0069
## 280 0.8416 nan 0.0500 -0.0059
## 300 0.7864 nan 0.0500 -0.0095
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8166 nan 0.0500 5.4690
## 2 52.9766 nan 0.0500 4.7273
## 3 48.5490 nan 0.0500 3.9240
## 4 44.3757 nan 0.0500 3.8684
## 5 40.6733 nan 0.0500 3.3667
## 6 37.1948 nan 0.0500 3.4309
## 7 34.1868 nan 0.0500 2.8596
## 8 31.4257 nan 0.0500 2.5529
## 9 28.8096 nan 0.0500 2.2876
## 10 26.4545 nan 0.0500 2.1163
## 20 12.6418 nan 0.0500 0.8235
## 40 4.6435 nan 0.0500 0.1253
## 60 3.0974 nan 0.0500 0.0176
## 80 2.5902 nan 0.0500 -0.0099
## 100 2.2615 nan 0.0500 0.0047
## 120 2.0305 nan 0.0500 -0.0127
## 140 1.8496 nan 0.0500 -0.0063
## 160 1.7091 nan 0.0500 -0.0193
## 180 1.5882 nan 0.0500 -0.0121
## 200 1.4789 nan 0.0500 -0.0049
## 220 1.3840 nan 0.0500 -0.0060
## 240 1.2883 nan 0.0500 -0.0091
## 260 1.2151 nan 0.0500 -0.0124
## 280 1.1527 nan 0.0500 -0.0075
## 300 1.0926 nan 0.0500 -0.0072
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.8439 nan 0.0500 5.4330
## 2 53.0236 nan 0.0500 5.1841
## 3 48.7113 nan 0.0500 4.0321
## 4 44.5724 nan 0.0500 3.9382
## 5 40.8458 nan 0.0500 3.9470
## 6 37.5173 nan 0.0500 2.8976
## 7 34.3400 nan 0.0500 3.1000
## 8 31.5673 nan 0.0500 2.5679
## 9 29.1831 nan 0.0500 2.3236
## 10 26.8846 nan 0.0500 2.1696
## 20 13.1125 nan 0.0500 0.8076
## 40 5.1167 nan 0.0500 0.1116
## 60 3.5455 nan 0.0500 0.0092
## 80 2.9536 nan 0.0500 -0.0039
## 100 2.6051 nan 0.0500 -0.0036
## 120 2.3859 nan 0.0500 -0.0082
## 140 2.2062 nan 0.0500 -0.0117
## 160 2.0454 nan 0.0500 -0.0075
## 180 1.9187 nan 0.0500 -0.0103
## 200 1.8172 nan 0.0500 -0.0137
## 220 1.7101 nan 0.0500 -0.0039
## 240 1.6247 nan 0.0500 -0.0143
## 260 1.5270 nan 0.0500 -0.0049
## 280 1.4606 nan 0.0500 -0.0197
## 300 1.3837 nan 0.0500 -0.0090
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1438 nan 0.1000 7.6410
## 2 49.6843 nan 0.1000 6.0802
## 3 43.7805 nan 0.1000 5.6168
## 4 39.0668 nan 0.1000 3.9554
## 5 35.4015 nan 0.1000 3.2502
## 6 31.6470 nan 0.1000 3.5191
## 7 28.8782 nan 0.1000 2.7394
## 8 26.1881 nan 0.1000 2.3165
## 9 23.8081 nan 0.1000 1.9816
## 10 21.5368 nan 0.1000 2.2400
## 20 11.1150 nan 0.1000 0.4144
## 40 5.4556 nan 0.1000 0.0768
## 60 4.1872 nan 0.1000 0.0067
## 80 3.8071 nan 0.1000 -0.0298
## 100 3.5672 nan 0.1000 -0.0086
## 120 3.4321 nan 0.1000 -0.0254
## 140 3.2762 nan 0.1000 -0.0119
## 160 3.1609 nan 0.1000 -0.0123
## 180 3.0841 nan 0.1000 -0.0098
## 200 3.0077 nan 0.1000 -0.0052
## 220 2.9371 nan 0.1000 -0.0359
## 240 2.8913 nan 0.1000 -0.0215
## 260 2.8233 nan 0.1000 -0.0148
## 280 2.7803 nan 0.1000 -0.0066
## 300 2.7299 nan 0.1000 -0.0101
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.8849 nan 0.1000 7.5552
## 2 49.8578 nan 0.1000 5.6390
## 3 44.5300 nan 0.1000 5.7536
## 4 40.2391 nan 0.1000 4.3531
## 5 35.9313 nan 0.1000 3.9622
## 6 32.4866 nan 0.1000 3.3353
## 7 29.1851 nan 0.1000 2.8554
## 8 26.4746 nan 0.1000 2.5684
## 9 24.0476 nan 0.1000 2.1929
## 10 22.2728 nan 0.1000 1.4910
## 20 11.0843 nan 0.1000 0.4982
## 40 5.4835 nan 0.1000 0.0019
## 60 4.2099 nan 0.1000 0.0249
## 80 3.8625 nan 0.1000 -0.0256
## 100 3.6130 nan 0.1000 -0.0086
## 120 3.4371 nan 0.1000 -0.0130
## 140 3.2897 nan 0.1000 -0.0045
## 160 3.1796 nan 0.1000 -0.0057
## 180 3.1010 nan 0.1000 -0.0033
## 200 3.0279 nan 0.1000 -0.0114
## 220 2.9605 nan 0.1000 -0.0274
## 240 2.8913 nan 0.1000 -0.0037
## 260 2.8233 nan 0.1000 -0.0040
## 280 2.7636 nan 0.1000 -0.0080
## 300 2.7211 nan 0.1000 -0.0071
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.9434 nan 0.1000 7.1297
## 2 49.3077 nan 0.1000 5.8306
## 3 43.7682 nan 0.1000 5.3651
## 4 39.4861 nan 0.1000 4.3868
## 5 35.4206 nan 0.1000 4.1375
## 6 31.8842 nan 0.1000 3.0288
## 7 28.9768 nan 0.1000 2.6172
## 8 26.6642 nan 0.1000 2.3271
## 9 24.1922 nan 0.1000 2.3758
## 10 22.1891 nan 0.1000 1.9361
## 20 11.4506 nan 0.1000 0.5568
## 40 5.6318 nan 0.1000 0.1027
## 60 4.4680 nan 0.1000 0.0143
## 80 4.0773 nan 0.1000 -0.0093
## 100 3.8534 nan 0.1000 -0.0392
## 120 3.6556 nan 0.1000 -0.0076
## 140 3.5054 nan 0.1000 -0.0039
## 160 3.3528 nan 0.1000 0.0001
## 180 3.2504 nan 0.1000 -0.0084
## 200 3.1698 nan 0.1000 -0.0244
## 220 3.0757 nan 0.1000 -0.0038
## 240 3.0177 nan 0.1000 -0.0069
## 260 2.9442 nan 0.1000 -0.0103
## 280 2.8672 nan 0.1000 -0.0272
## 300 2.7861 nan 0.1000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.7412 nan 0.1000 8.9841
## 2 44.3088 nan 0.1000 7.7404
## 3 37.1985 nan 0.1000 6.9160
## 4 31.9109 nan 0.1000 5.3672
## 5 27.2800 nan 0.1000 4.0731
## 6 23.2697 nan 0.1000 3.5761
## 7 20.2830 nan 0.1000 3.1753
## 8 17.8072 nan 0.1000 2.3982
## 9 15.6642 nan 0.1000 1.8860
## 10 13.7190 nan 0.1000 1.9197
## 20 5.5605 nan 0.1000 0.3615
## 40 3.1109 nan 0.1000 0.0152
## 60 2.5465 nan 0.1000 -0.0117
## 80 2.1519 nan 0.1000 -0.0449
## 100 1.8751 nan 0.1000 -0.0148
## 120 1.7096 nan 0.1000 -0.0138
## 140 1.5726 nan 0.1000 -0.0331
## 160 1.4501 nan 0.1000 -0.0174
## 180 1.3180 nan 0.1000 -0.0102
## 200 1.1941 nan 0.1000 -0.0185
## 220 1.1013 nan 0.1000 -0.0145
## 240 1.0137 nan 0.1000 -0.0221
## 260 0.9558 nan 0.1000 -0.0062
## 280 0.8985 nan 0.1000 -0.0130
## 300 0.8435 nan 0.1000 -0.0068
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.3344 nan 0.1000 9.9743
## 2 45.0409 nan 0.1000 8.3071
## 3 38.1850 nan 0.1000 6.7013
## 4 32.6666 nan 0.1000 5.0264
## 5 27.8562 nan 0.1000 4.4839
## 6 24.1889 nan 0.1000 3.7144
## 7 20.9650 nan 0.1000 3.1286
## 8 18.5104 nan 0.1000 2.4828
## 9 16.2899 nan 0.1000 2.2134
## 10 14.4691 nan 0.1000 1.5834
## 20 5.8636 nan 0.1000 0.3076
## 40 3.1876 nan 0.1000 -0.0456
## 60 2.6828 nan 0.1000 -0.0121
## 80 2.2741 nan 0.1000 -0.0360
## 100 2.0673 nan 0.1000 -0.0053
## 120 1.8775 nan 0.1000 -0.0226
## 140 1.7333 nan 0.1000 -0.0471
## 160 1.6139 nan 0.1000 -0.0163
## 180 1.5099 nan 0.1000 -0.0066
## 200 1.4060 nan 0.1000 -0.0238
## 220 1.3298 nan 0.1000 -0.0185
## 240 1.2528 nan 0.1000 -0.0123
## 260 1.1764 nan 0.1000 -0.0145
## 280 1.1199 nan 0.1000 -0.0250
## 300 1.0599 nan 0.1000 -0.0105
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0915 nan 0.1000 10.5705
## 2 45.0014 nan 0.1000 8.4517
## 3 37.9915 nan 0.1000 6.7091
## 4 32.4900 nan 0.1000 4.9277
## 5 27.9204 nan 0.1000 4.3298
## 6 23.9782 nan 0.1000 3.6528
## 7 20.8991 nan 0.1000 2.9252
## 8 18.2168 nan 0.1000 2.6738
## 9 15.9633 nan 0.1000 2.0135
## 10 14.1448 nan 0.1000 1.7032
## 20 5.9339 nan 0.1000 0.3635
## 40 3.4261 nan 0.1000 0.0246
## 60 2.8715 nan 0.1000 -0.0046
## 80 2.4889 nan 0.1000 -0.0150
## 100 2.2028 nan 0.1000 -0.0221
## 120 2.0199 nan 0.1000 -0.0183
## 140 1.8795 nan 0.1000 -0.0098
## 160 1.7380 nan 0.1000 -0.0168
## 180 1.6384 nan 0.1000 -0.0176
## 200 1.5427 nan 0.1000 -0.0064
## 220 1.4781 nan 0.1000 -0.0076
## 240 1.3925 nan 0.1000 -0.0115
## 260 1.3170 nan 0.1000 -0.0182
## 280 1.2523 nan 0.1000 -0.0174
## 300 1.2009 nan 0.1000 -0.0113
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.8281 nan 0.1000 9.3216
## 2 44.3513 nan 0.1000 7.7461
## 3 36.9729 nan 0.1000 6.8963
## 4 31.1202 nan 0.1000 5.2217
## 5 26.5263 nan 0.1000 4.6090
## 6 22.4745 nan 0.1000 3.6961
## 7 19.4208 nan 0.1000 3.2717
## 8 16.6406 nan 0.1000 2.5089
## 9 14.3394 nan 0.1000 2.0172
## 10 12.4630 nan 0.1000 1.2673
## 20 4.4724 nan 0.1000 0.2713
## 40 2.3520 nan 0.1000 0.0099
## 60 1.7759 nan 0.1000 -0.0302
## 80 1.4284 nan 0.1000 -0.0179
## 100 1.2134 nan 0.1000 -0.0193
## 120 1.0223 nan 0.1000 -0.0267
## 140 0.8623 nan 0.1000 -0.0044
## 160 0.7468 nan 0.1000 -0.0131
## 180 0.6454 nan 0.1000 -0.0061
## 200 0.5733 nan 0.1000 -0.0134
## 220 0.5096 nan 0.1000 -0.0125
## 240 0.4575 nan 0.1000 -0.0118
## 260 0.4090 nan 0.1000 -0.0093
## 280 0.3661 nan 0.1000 -0.0099
## 300 0.3285 nan 0.1000 -0.0085
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.9475 nan 0.1000 9.9414
## 2 44.3752 nan 0.1000 7.5275
## 3 37.1191 nan 0.1000 7.2150
## 4 31.2794 nan 0.1000 5.8318
## 5 26.3339 nan 0.1000 4.9722
## 6 22.3651 nan 0.1000 3.9869
## 7 19.0259 nan 0.1000 3.2341
## 8 16.5154 nan 0.1000 2.3179
## 9 14.4399 nan 0.1000 2.2921
## 10 12.6663 nan 0.1000 1.6430
## 20 4.6739 nan 0.1000 0.2106
## 40 2.6628 nan 0.1000 -0.0120
## 60 2.1095 nan 0.1000 -0.0118
## 80 1.8062 nan 0.1000 -0.0316
## 100 1.5762 nan 0.1000 -0.0224
## 120 1.3809 nan 0.1000 -0.0215
## 140 1.2163 nan 0.1000 -0.0200
## 160 1.0979 nan 0.1000 -0.0126
## 180 0.9896 nan 0.1000 -0.0176
## 200 0.8953 nan 0.1000 -0.0109
## 220 0.8216 nan 0.1000 -0.0189
## 240 0.7489 nan 0.1000 -0.0143
## 260 0.6933 nan 0.1000 -0.0114
## 280 0.6449 nan 0.1000 -0.0115
## 300 0.5809 nan 0.1000 -0.0081
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.2363 nan 0.1000 11.4975
## 2 43.7069 nan 0.1000 8.8512
## 3 36.6773 nan 0.1000 6.5151
## 4 30.7827 nan 0.1000 5.4660
## 5 26.2092 nan 0.1000 4.3566
## 6 22.4510 nan 0.1000 3.9423
## 7 19.2072 nan 0.1000 3.1462
## 8 16.8900 nan 0.1000 2.1972
## 9 14.5968 nan 0.1000 2.2840
## 10 12.9082 nan 0.1000 1.7076
## 20 5.0341 nan 0.1000 0.2392
## 40 3.0711 nan 0.1000 0.0147
## 60 2.5345 nan 0.1000 -0.0565
## 80 2.2039 nan 0.1000 -0.0094
## 100 1.9339 nan 0.1000 -0.0273
## 120 1.7305 nan 0.1000 -0.0242
## 140 1.5635 nan 0.1000 -0.0267
## 160 1.4299 nan 0.1000 -0.0267
## 180 1.3241 nan 0.1000 -0.0262
## 200 1.2199 nan 0.1000 -0.0199
## 220 1.1367 nan 0.1000 -0.0131
## 240 1.0470 nan 0.1000 -0.0157
## 260 0.9730 nan 0.1000 -0.0071
## 280 0.9186 nan 0.1000 -0.0094
## 300 0.8576 nan 0.1000 -0.0109
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.4123 nan 0.0100 0.7552
## 2 60.6847 nan 0.0100 0.7746
## 3 59.9616 nan 0.0100 0.7510
## 4 59.2769 nan 0.0100 0.7578
## 5 58.5815 nan 0.0100 0.7104
## 6 57.8613 nan 0.0100 0.7247
## 7 57.1910 nan 0.0100 0.7041
## 8 56.5057 nan 0.0100 0.6832
## 9 55.8467 nan 0.0100 0.6842
## 10 55.1497 nan 0.0100 0.6449
## 20 49.2898 nan 0.0100 0.4414
## 40 39.7083 nan 0.0100 0.4003
## 60 32.4471 nan 0.0100 0.2922
## 80 26.9294 nan 0.0100 0.2139
## 100 22.7901 nan 0.0100 0.1500
## 120 19.4928 nan 0.0100 0.1305
## 140 16.8889 nan 0.0100 0.1103
## 160 14.8397 nan 0.0100 0.0889
## 180 13.0977 nan 0.0100 0.0621
## 200 11.6923 nan 0.0100 0.0463
## 220 10.5519 nan 0.0100 0.0455
## 240 9.5875 nan 0.0100 0.0347
## 260 8.7787 nan 0.0100 0.0288
## 280 8.0850 nan 0.0100 0.0200
## 300 7.4825 nan 0.0100 0.0238
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.3597 nan 0.0100 0.7736
## 2 60.5635 nan 0.0100 0.7617
## 3 59.7441 nan 0.0100 0.7398
## 4 58.9336 nan 0.0100 0.7469
## 5 58.2203 nan 0.0100 0.7285
## 6 57.4655 nan 0.0100 0.7159
## 7 56.6781 nan 0.0100 0.6548
## 8 56.0807 nan 0.0100 0.6583
## 9 55.4060 nan 0.0100 0.6756
## 10 54.7378 nan 0.0100 0.6507
## 20 48.7677 nan 0.0100 0.5336
## 40 39.2732 nan 0.0100 0.3985
## 60 32.2951 nan 0.0100 0.3395
## 80 26.8741 nan 0.0100 0.2305
## 100 22.6560 nan 0.0100 0.1701
## 120 19.3996 nan 0.0100 0.1340
## 140 16.8076 nan 0.0100 0.1056
## 160 14.7149 nan 0.0100 0.0792
## 180 13.0342 nan 0.0100 0.0496
## 200 11.6096 nan 0.0100 0.0485
## 220 10.4521 nan 0.0100 0.0438
## 240 9.4651 nan 0.0100 0.0211
## 260 8.6537 nan 0.0100 0.0371
## 280 7.9659 nan 0.0100 0.0266
## 300 7.3938 nan 0.0100 0.0216
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.2880 nan 0.0100 0.8177
## 2 60.5290 nan 0.0100 0.7330
## 3 59.8133 nan 0.0100 0.7353
## 4 59.0411 nan 0.0100 0.7028
## 5 58.3450 nan 0.0100 0.6948
## 6 57.6646 nan 0.0100 0.6865
## 7 56.9669 nan 0.0100 0.7072
## 8 56.3224 nan 0.0100 0.6302
## 9 55.6263 nan 0.0100 0.6523
## 10 54.9537 nan 0.0100 0.6061
## 20 48.9212 nan 0.0100 0.5676
## 40 39.5169 nan 0.0100 0.3862
## 60 32.2676 nan 0.0100 0.3032
## 80 26.9242 nan 0.0100 0.2374
## 100 22.7466 nan 0.0100 0.1695
## 120 19.5099 nan 0.0100 0.1342
## 140 16.9995 nan 0.0100 0.1033
## 160 14.8824 nan 0.0100 0.0782
## 180 13.2226 nan 0.0100 0.0483
## 200 11.8360 nan 0.0100 0.0641
## 220 10.6695 nan 0.0100 0.0409
## 240 9.6687 nan 0.0100 0.0305
## 260 8.8797 nan 0.0100 0.0286
## 280 8.1734 nan 0.0100 0.0274
## 300 7.5685 nan 0.0100 0.0168
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0977 nan 0.0100 0.9427
## 2 60.0594 nan 0.0100 1.0613
## 3 59.0918 nan 0.0100 1.0262
## 4 58.1146 nan 0.0100 0.8663
## 5 57.1321 nan 0.0100 0.9276
## 6 56.2013 nan 0.0100 0.9042
## 7 55.3087 nan 0.0100 0.9600
## 8 54.3788 nan 0.0100 0.9244
## 9 53.4878 nan 0.0100 0.8785
## 10 52.6386 nan 0.0100 0.9086
## 20 44.8154 nan 0.0100 0.7210
## 40 32.8350 nan 0.0100 0.4899
## 60 24.4946 nan 0.0100 0.3370
## 80 18.7028 nan 0.0100 0.2310
## 100 14.6219 nan 0.0100 0.1863
## 120 11.5693 nan 0.0100 0.1186
## 140 9.4305 nan 0.0100 0.0736
## 160 7.8682 nan 0.0100 0.0646
## 180 6.6711 nan 0.0100 0.0428
## 200 5.7766 nan 0.0100 0.0313
## 220 5.1078 nan 0.0100 0.0212
## 240 4.6183 nan 0.0100 0.0042
## 260 4.2265 nan 0.0100 0.0123
## 280 3.9328 nan 0.0100 0.0074
## 300 3.6914 nan 0.0100 0.0074
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1732 nan 0.0100 0.9445
## 2 60.1957 nan 0.0100 0.9623
## 3 59.1990 nan 0.0100 0.8884
## 4 58.2403 nan 0.0100 0.9414
## 5 57.3062 nan 0.0100 0.8516
## 6 56.3683 nan 0.0100 0.8758
## 7 55.4381 nan 0.0100 0.9874
## 8 54.5488 nan 0.0100 0.8425
## 9 53.6730 nan 0.0100 0.8442
## 10 52.7985 nan 0.0100 0.8567
## 20 44.9028 nan 0.0100 0.6723
## 40 32.8287 nan 0.0100 0.5122
## 60 24.4930 nan 0.0100 0.3126
## 80 18.7801 nan 0.0100 0.2517
## 100 14.7229 nan 0.0100 0.1706
## 120 11.7417 nan 0.0100 0.1077
## 140 9.5099 nan 0.0100 0.0873
## 160 7.9306 nan 0.0100 0.0537
## 180 6.7694 nan 0.0100 0.0473
## 200 5.8805 nan 0.0100 0.0343
## 220 5.2281 nan 0.0100 0.0260
## 240 4.7385 nan 0.0100 0.0115
## 260 4.3735 nan 0.0100 0.0150
## 280 4.0978 nan 0.0100 0.0072
## 300 3.8860 nan 0.0100 0.0060
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.1186 nan 0.0100 1.0299
## 2 60.0822 nan 0.0100 0.9548
## 3 59.1150 nan 0.0100 0.9146
## 4 58.1126 nan 0.0100 1.0974
## 5 57.1264 nan 0.0100 0.8615
## 6 56.1828 nan 0.0100 0.8528
## 7 55.3212 nan 0.0100 0.8592
## 8 54.4465 nan 0.0100 0.8176
## 9 53.5818 nan 0.0100 0.8348
## 10 52.6712 nan 0.0100 0.9044
## 20 44.7625 nan 0.0100 0.7289
## 40 32.8066 nan 0.0100 0.4794
## 60 24.4810 nan 0.0100 0.3705
## 80 18.6729 nan 0.0100 0.1756
## 100 14.6467 nan 0.0100 0.1584
## 120 11.7640 nan 0.0100 0.1256
## 140 9.6319 nan 0.0100 0.0743
## 160 8.0970 nan 0.0100 0.0634
## 180 6.9714 nan 0.0100 0.0409
## 200 6.0629 nan 0.0100 0.0347
## 220 5.3935 nan 0.0100 0.0242
## 240 4.8824 nan 0.0100 0.0170
## 260 4.5032 nan 0.0100 0.0138
## 280 4.2157 nan 0.0100 0.0069
## 300 4.0079 nan 0.0100 0.0055
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0181 nan 0.0100 1.1632
## 2 59.9494 nan 0.0100 0.9765
## 3 58.8971 nan 0.0100 0.9996
## 4 57.8735 nan 0.0100 1.0641
## 5 56.8328 nan 0.0100 1.0347
## 6 55.7976 nan 0.0100 0.9063
## 7 54.8582 nan 0.0100 0.9821
## 8 53.9091 nan 0.0100 0.8822
## 9 52.9542 nan 0.0100 0.9450
## 10 52.0128 nan 0.0100 0.9283
## 20 43.7734 nan 0.0100 0.7030
## 40 31.2970 nan 0.0100 0.5270
## 60 22.7000 nan 0.0100 0.3558
## 80 16.7886 nan 0.0100 0.2125
## 100 12.7738 nan 0.0100 0.1402
## 120 9.8961 nan 0.0100 0.1199
## 140 7.8415 nan 0.0100 0.0743
## 160 6.4162 nan 0.0100 0.0461
## 180 5.3918 nan 0.0100 0.0276
## 200 4.6298 nan 0.0100 0.0222
## 220 4.0768 nan 0.0100 0.0169
## 240 3.6884 nan 0.0100 0.0095
## 260 3.3903 nan 0.0100 0.0057
## 280 3.1447 nan 0.0100 0.0033
## 300 2.9599 nan 0.0100 -0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0860 nan 0.0100 1.0078
## 2 59.9509 nan 0.0100 0.8955
## 3 58.8792 nan 0.0100 0.9906
## 4 57.8356 nan 0.0100 0.9603
## 5 56.8157 nan 0.0100 0.9592
## 6 55.8189 nan 0.0100 0.9664
## 7 54.8035 nan 0.0100 0.9628
## 8 53.8682 nan 0.0100 0.8291
## 9 52.9132 nan 0.0100 1.0020
## 10 51.9945 nan 0.0100 0.7970
## 20 43.8395 nan 0.0100 0.7837
## 40 31.5341 nan 0.0100 0.5718
## 60 22.8647 nan 0.0100 0.2933
## 80 17.0311 nan 0.0100 0.2382
## 100 12.9416 nan 0.0100 0.1413
## 120 10.1329 nan 0.0100 0.1129
## 140 8.1037 nan 0.0100 0.0748
## 160 6.6616 nan 0.0100 0.0520
## 180 5.5995 nan 0.0100 0.0487
## 200 4.8319 nan 0.0100 0.0249
## 220 4.2943 nan 0.0100 0.0135
## 240 3.8933 nan 0.0100 0.0162
## 260 3.6135 nan 0.0100 0.0003
## 280 3.3702 nan 0.0100 0.0020
## 300 3.1901 nan 0.0100 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 61.0997 nan 0.0100 1.1448
## 2 60.0816 nan 0.0100 1.0968
## 3 59.1176 nan 0.0100 1.0778
## 4 58.0978 nan 0.0100 1.0203
## 5 57.0813 nan 0.0100 1.0607
## 6 56.0957 nan 0.0100 0.9857
## 7 55.1123 nan 0.0100 0.9380
## 8 54.2166 nan 0.0100 0.9265
## 9 53.2753 nan 0.0100 0.8730
## 10 52.3434 nan 0.0100 0.9844
## 20 44.2088 nan 0.0100 0.6842
## 40 31.6618 nan 0.0100 0.5597
## 60 23.2728 nan 0.0100 0.3441
## 80 17.3936 nan 0.0100 0.2397
## 100 13.2763 nan 0.0100 0.1328
## 120 10.4387 nan 0.0100 0.1139
## 140 8.3681 nan 0.0100 0.0963
## 160 6.9513 nan 0.0100 0.0513
## 180 5.9267 nan 0.0100 0.0384
## 200 5.1747 nan 0.0100 0.0271
## 220 4.6475 nan 0.0100 0.0155
## 240 4.2646 nan 0.0100 0.0093
## 260 3.9615 nan 0.0100 0.0075
## 280 3.7386 nan 0.0100 -0.0005
## 300 3.5676 nan 0.0100 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.1881 nan 0.0500 3.8208
## 2 54.5427 nan 0.0500 3.4755
## 3 51.4488 nan 0.0500 3.0136
## 4 48.6118 nan 0.0500 3.0824
## 5 45.8034 nan 0.0500 2.7056
## 6 43.4423 nan 0.0500 2.1932
## 7 41.2264 nan 0.0500 2.2131
## 8 39.1029 nan 0.0500 1.7811
## 9 37.1368 nan 0.0500 2.2201
## 10 35.3039 nan 0.0500 1.6741
## 20 23.1465 nan 0.0500 0.7675
## 40 11.6412 nan 0.0500 0.3018
## 60 7.5487 nan 0.0500 0.0720
## 80 5.5792 nan 0.0500 0.0561
## 100 4.5669 nan 0.0500 0.0096
## 120 4.0937 nan 0.0500 0.0067
## 140 3.8254 nan 0.0500 -0.0052
## 160 3.6798 nan 0.0500 -0.0035
## 180 3.5808 nan 0.0500 -0.0064
## 200 3.4919 nan 0.0500 -0.0052
## 220 3.4185 nan 0.0500 -0.0022
## 240 3.3672 nan 0.0500 -0.0002
## 260 3.3115 nan 0.0500 -0.0156
## 280 3.2659 nan 0.0500 -0.0050
## 300 3.2192 nan 0.0500 -0.0146
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0602 nan 0.0500 3.8358
## 2 54.4010 nan 0.0500 3.2404
## 3 51.3143 nan 0.0500 2.8677
## 4 48.0191 nan 0.0500 2.7982
## 5 45.3547 nan 0.0500 2.5488
## 6 43.0056 nan 0.0500 2.4537
## 7 40.5589 nan 0.0500 2.1190
## 8 38.4296 nan 0.0500 1.9977
## 9 36.2655 nan 0.0500 1.7318
## 10 34.5600 nan 0.0500 1.7133
## 20 22.3853 nan 0.0500 1.0161
## 40 11.7448 nan 0.0500 0.2635
## 60 7.4733 nan 0.0500 0.0369
## 80 5.5585 nan 0.0500 0.0248
## 100 4.6152 nan 0.0500 0.0273
## 120 4.1724 nan 0.0500 0.0042
## 140 3.9429 nan 0.0500 -0.0043
## 160 3.8223 nan 0.0500 -0.0179
## 180 3.7057 nan 0.0500 -0.0103
## 200 3.6266 nan 0.0500 -0.0099
## 220 3.5412 nan 0.0500 -0.0090
## 240 3.4828 nan 0.0500 0.0007
## 260 3.4252 nan 0.0500 -0.0005
## 280 3.3829 nan 0.0500 -0.0073
## 300 3.3251 nan 0.0500 -0.0038
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.2653 nan 0.0500 3.9766
## 2 54.8815 nan 0.0500 3.1078
## 3 51.8309 nan 0.0500 3.3556
## 4 48.7801 nan 0.0500 2.8763
## 5 46.2317 nan 0.0500 2.6096
## 6 43.7682 nan 0.0500 2.3220
## 7 41.3816 nan 0.0500 2.2582
## 8 39.2572 nan 0.0500 2.0708
## 9 37.0796 nan 0.0500 2.2355
## 10 35.2031 nan 0.0500 1.9405
## 20 22.6753 nan 0.0500 0.7557
## 40 12.0534 nan 0.0500 0.2027
## 60 7.6379 nan 0.0500 0.1136
## 80 5.7225 nan 0.0500 0.0519
## 100 4.8653 nan 0.0500 -0.0086
## 120 4.4377 nan 0.0500 -0.0108
## 140 4.2375 nan 0.0500 0.0003
## 160 4.1094 nan 0.0500 -0.0111
## 180 4.0002 nan 0.0500 -0.0061
## 200 3.9106 nan 0.0500 -0.0020
## 220 3.8325 nan 0.0500 -0.0031
## 240 3.7628 nan 0.0500 -0.0103
## 260 3.7043 nan 0.0500 -0.0033
## 280 3.6272 nan 0.0500 -0.0048
## 300 3.5592 nan 0.0500 -0.0094
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.0438 nan 0.0500 4.5934
## 2 52.4670 nan 0.0500 4.2808
## 3 48.1446 nan 0.0500 3.7412
## 4 44.4282 nan 0.0500 3.9592
## 5 40.9729 nan 0.0500 3.3759
## 6 37.9089 nan 0.0500 3.0781
## 7 35.0891 nan 0.0500 2.8219
## 8 32.4289 nan 0.0500 2.7141
## 9 29.8832 nan 0.0500 2.4005
## 10 27.6554 nan 0.0500 1.8928
## 20 14.3098 nan 0.0500 0.8156
## 40 5.8797 nan 0.0500 0.1527
## 60 3.7304 nan 0.0500 0.0196
## 80 3.1475 nan 0.0500 -0.0154
## 100 2.8373 nan 0.0500 -0.0043
## 120 2.6050 nan 0.0500 -0.0210
## 140 2.4566 nan 0.0500 -0.0091
## 160 2.3281 nan 0.0500 -0.0029
## 180 2.1866 nan 0.0500 -0.0067
## 200 2.0541 nan 0.0500 -0.0039
## 220 1.9187 nan 0.0500 -0.0061
## 240 1.8218 nan 0.0500 -0.0109
## 260 1.7311 nan 0.0500 -0.0104
## 280 1.6543 nan 0.0500 -0.0069
## 300 1.5843 nan 0.0500 -0.0087
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8417 nan 0.0500 4.5411
## 2 52.3757 nan 0.0500 4.3217
## 3 48.1222 nan 0.0500 3.7584
## 4 44.3999 nan 0.0500 3.6053
## 5 41.0076 nan 0.0500 3.0017
## 6 37.8972 nan 0.0500 3.2062
## 7 35.0140 nan 0.0500 2.8904
## 8 32.3628 nan 0.0500 2.6887
## 9 29.9268 nan 0.0500 2.1858
## 10 27.7878 nan 0.0500 1.7528
## 20 14.4872 nan 0.0500 0.9270
## 40 5.7867 nan 0.0500 0.1248
## 60 3.8092 nan 0.0500 0.0091
## 80 3.2938 nan 0.0500 -0.0076
## 100 2.9802 nan 0.0500 -0.0080
## 120 2.7779 nan 0.0500 -0.0020
## 140 2.6243 nan 0.0500 -0.0292
## 160 2.4941 nan 0.0500 -0.0200
## 180 2.3758 nan 0.0500 -0.0170
## 200 2.2624 nan 0.0500 -0.0123
## 220 2.1762 nan 0.0500 -0.0141
## 240 2.0804 nan 0.0500 -0.0096
## 260 2.0210 nan 0.0500 -0.0121
## 280 1.9341 nan 0.0500 -0.0117
## 300 1.8632 nan 0.0500 -0.0080
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.4354 nan 0.0500 4.9324
## 2 52.6856 nan 0.0500 4.5665
## 3 48.5819 nan 0.0500 3.7521
## 4 44.7524 nan 0.0500 4.1088
## 5 41.1739 nan 0.0500 3.5443
## 6 38.1425 nan 0.0500 3.2636
## 7 35.2090 nan 0.0500 2.7641
## 8 32.4684 nan 0.0500 2.6513
## 9 29.8403 nan 0.0500 2.4102
## 10 27.6731 nan 0.0500 2.2611
## 20 14.3729 nan 0.0500 0.8711
## 40 6.1089 nan 0.0500 0.1803
## 60 4.1491 nan 0.0500 0.0240
## 80 3.5431 nan 0.0500 -0.0110
## 100 3.2463 nan 0.0500 -0.0157
## 120 3.0362 nan 0.0500 -0.0111
## 140 2.8519 nan 0.0500 -0.0163
## 160 2.7025 nan 0.0500 -0.0193
## 180 2.5846 nan 0.0500 -0.0037
## 200 2.4819 nan 0.0500 -0.0041
## 220 2.3853 nan 0.0500 -0.0085
## 240 2.2949 nan 0.0500 -0.0092
## 260 2.2406 nan 0.0500 -0.0161
## 280 2.1830 nan 0.0500 -0.0093
## 300 2.1130 nan 0.0500 -0.0128
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7797 nan 0.0500 5.3080
## 2 51.7221 nan 0.0500 4.7184
## 3 47.3592 nan 0.0500 4.2967
## 4 43.3355 nan 0.0500 3.5109
## 5 39.6401 nan 0.0500 3.7149
## 6 36.5652 nan 0.0500 3.2352
## 7 33.4771 nan 0.0500 2.6516
## 8 30.9219 nan 0.0500 2.1424
## 9 28.2716 nan 0.0500 2.6244
## 10 26.0370 nan 0.0500 2.4572
## 20 12.4302 nan 0.0500 0.7796
## 40 4.5469 nan 0.0500 0.1346
## 60 2.9627 nan 0.0500 -0.0285
## 80 2.4488 nan 0.0500 -0.0064
## 100 2.1305 nan 0.0500 0.0015
## 120 1.9266 nan 0.0500 -0.0264
## 140 1.7322 nan 0.0500 -0.0261
## 160 1.5931 nan 0.0500 -0.0223
## 180 1.4557 nan 0.0500 -0.0110
## 200 1.3342 nan 0.0500 -0.0044
## 220 1.2308 nan 0.0500 -0.0125
## 240 1.1412 nan 0.0500 -0.0086
## 260 1.0518 nan 0.0500 -0.0058
## 280 0.9758 nan 0.0500 -0.0045
## 300 0.9112 nan 0.0500 -0.0121
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8052 nan 0.0500 5.1934
## 2 51.9576 nan 0.0500 4.7822
## 3 47.6191 nan 0.0500 4.2905
## 4 43.7178 nan 0.0500 4.0496
## 5 39.9553 nan 0.0500 3.4817
## 6 36.8328 nan 0.0500 3.2097
## 7 33.8109 nan 0.0500 3.1067
## 8 31.0541 nan 0.0500 2.6924
## 9 28.7235 nan 0.0500 2.2073
## 10 26.4528 nan 0.0500 2.2677
## 20 12.5358 nan 0.0500 0.8226
## 40 4.6955 nan 0.0500 0.1092
## 60 3.1561 nan 0.0500 0.0207
## 80 2.6748 nan 0.0500 -0.0184
## 100 2.4147 nan 0.0500 -0.0238
## 120 2.2434 nan 0.0500 -0.0111
## 140 2.0750 nan 0.0500 -0.0276
## 160 1.9281 nan 0.0500 -0.0150
## 180 1.7972 nan 0.0500 -0.0111
## 200 1.6919 nan 0.0500 -0.0111
## 220 1.5931 nan 0.0500 -0.0073
## 240 1.5182 nan 0.0500 -0.0117
## 260 1.4274 nan 0.0500 -0.0136
## 280 1.3609 nan 0.0500 -0.0040
## 300 1.2874 nan 0.0500 -0.0084
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.8488 nan 0.0500 5.2633
## 2 52.1349 nan 0.0500 5.0083
## 3 47.7429 nan 0.0500 4.3664
## 4 43.6825 nan 0.0500 3.5465
## 5 40.0605 nan 0.0500 3.6206
## 6 36.7007 nan 0.0500 2.8452
## 7 33.6879 nan 0.0500 2.6694
## 8 30.9395 nan 0.0500 2.5053
## 9 28.5009 nan 0.0500 2.2607
## 10 26.3635 nan 0.0500 1.9661
## 20 12.9831 nan 0.0500 0.9210
## 40 5.0551 nan 0.0500 0.1290
## 60 3.5914 nan 0.0500 0.0214
## 80 3.0735 nan 0.0500 -0.0181
## 100 2.8128 nan 0.0500 -0.0209
## 120 2.5975 nan 0.0500 -0.0056
## 140 2.4276 nan 0.0500 -0.0124
## 160 2.2774 nan 0.0500 -0.0072
## 180 2.1701 nan 0.0500 -0.0080
## 200 2.0776 nan 0.0500 -0.0096
## 220 1.9822 nan 0.0500 -0.0170
## 240 1.8783 nan 0.0500 -0.0061
## 260 1.8112 nan 0.0500 -0.0115
## 280 1.7343 nan 0.0500 -0.0112
## 300 1.6591 nan 0.0500 -0.0067
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.8108 nan 0.1000 7.5007
## 2 48.0209 nan 0.1000 6.3378
## 3 43.1623 nan 0.1000 5.0967
## 4 38.6243 nan 0.1000 3.6919
## 5 34.6969 nan 0.1000 3.4864
## 6 31.6103 nan 0.1000 2.6766
## 7 28.7867 nan 0.1000 2.7828
## 8 26.4870 nan 0.1000 1.9932
## 9 23.9992 nan 0.1000 2.3550
## 10 22.0016 nan 0.1000 1.8610
## 20 11.0804 nan 0.1000 0.5230
## 40 5.3736 nan 0.1000 0.0995
## 60 4.1375 nan 0.1000 -0.0190
## 80 3.7725 nan 0.1000 -0.0368
## 100 3.5996 nan 0.1000 -0.0025
## 120 3.4697 nan 0.1000 -0.0097
## 140 3.3533 nan 0.1000 -0.0339
## 160 3.2347 nan 0.1000 -0.0025
## 180 3.1362 nan 0.1000 -0.0062
## 200 3.0574 nan 0.1000 -0.0078
## 220 2.9869 nan 0.1000 -0.0235
## 240 2.9183 nan 0.1000 -0.0226
## 260 2.8669 nan 0.1000 -0.0151
## 280 2.8035 nan 0.1000 -0.0129
## 300 2.7439 nan 0.1000 -0.0238
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.3443 nan 0.1000 6.8967
## 2 48.0079 nan 0.1000 5.9306
## 3 43.0792 nan 0.1000 4.6852
## 4 38.6414 nan 0.1000 4.3885
## 5 34.9396 nan 0.1000 3.2151
## 6 31.6789 nan 0.1000 2.4089
## 7 28.5329 nan 0.1000 3.0761
## 8 26.0846 nan 0.1000 2.2409
## 9 23.9617 nan 0.1000 2.0799
## 10 22.2316 nan 0.1000 1.8288
## 20 11.5716 nan 0.1000 0.3759
## 40 5.4864 nan 0.1000 0.0303
## 60 4.2544 nan 0.1000 0.0024
## 80 3.9044 nan 0.1000 -0.0193
## 100 3.7197 nan 0.1000 -0.0127
## 120 3.5601 nan 0.1000 -0.0130
## 140 3.4519 nan 0.1000 -0.0144
## 160 3.3760 nan 0.1000 -0.0096
## 180 3.3122 nan 0.1000 -0.0228
## 200 3.2220 nan 0.1000 -0.0170
## 220 3.1553 nan 0.1000 -0.0140
## 240 3.1171 nan 0.1000 -0.0422
## 260 3.0352 nan 0.1000 -0.0182
## 280 2.9947 nan 0.1000 -0.0119
## 300 2.9614 nan 0.1000 -0.0023
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.7861 nan 0.1000 7.5744
## 2 49.2234 nan 0.1000 5.6367
## 3 44.1796 nan 0.1000 4.2563
## 4 38.9259 nan 0.1000 5.0769
## 5 34.7983 nan 0.1000 3.8742
## 6 31.5173 nan 0.1000 3.5578
## 7 28.8699 nan 0.1000 2.6807
## 8 26.3956 nan 0.1000 2.2813
## 9 23.9863 nan 0.1000 1.9707
## 10 22.0448 nan 0.1000 1.8542
## 20 11.3213 nan 0.1000 0.5519
## 40 5.7017 nan 0.1000 0.0397
## 60 4.5587 nan 0.1000 0.0324
## 80 4.1787 nan 0.1000 -0.0074
## 100 3.9631 nan 0.1000 -0.0009
## 120 3.8200 nan 0.1000 -0.0306
## 140 3.7215 nan 0.1000 -0.0309
## 160 3.6270 nan 0.1000 -0.0148
## 180 3.5313 nan 0.1000 -0.0176
## 200 3.4447 nan 0.1000 -0.0205
## 220 3.3815 nan 0.1000 -0.0170
## 240 3.3005 nan 0.1000 -0.0038
## 260 3.2531 nan 0.1000 -0.0163
## 280 3.1755 nan 0.1000 -0.0069
## 300 3.1208 nan 0.1000 -0.0065
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.3223 nan 0.1000 8.8464
## 2 44.6657 nan 0.1000 7.3540
## 3 38.1758 nan 0.1000 6.7436
## 4 32.4947 nan 0.1000 5.6730
## 5 27.7318 nan 0.1000 4.9662
## 6 23.9998 nan 0.1000 3.4460
## 7 20.8132 nan 0.1000 3.0447
## 8 18.1872 nan 0.1000 2.6695
## 9 15.9176 nan 0.1000 2.1656
## 10 14.1803 nan 0.1000 1.7077
## 20 5.8587 nan 0.1000 0.2893
## 40 3.0812 nan 0.1000 -0.0118
## 60 2.5710 nan 0.1000 -0.0556
## 80 2.2303 nan 0.1000 -0.0287
## 100 1.9870 nan 0.1000 -0.0142
## 120 1.8109 nan 0.1000 -0.0182
## 140 1.6438 nan 0.1000 -0.0106
## 160 1.5217 nan 0.1000 -0.0321
## 180 1.4113 nan 0.1000 -0.0318
## 200 1.3098 nan 0.1000 -0.0207
## 220 1.2040 nan 0.1000 -0.0293
## 240 1.1049 nan 0.1000 -0.0143
## 260 1.0161 nan 0.1000 -0.0049
## 280 0.9439 nan 0.1000 -0.0135
## 300 0.8844 nan 0.1000 -0.0166
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4021 nan 0.1000 9.0083
## 2 44.2496 nan 0.1000 8.2822
## 3 37.3361 nan 0.1000 5.9655
## 4 31.9045 nan 0.1000 5.0172
## 5 27.4969 nan 0.1000 4.2389
## 6 23.5336 nan 0.1000 3.3599
## 7 20.5659 nan 0.1000 2.9698
## 8 17.9630 nan 0.1000 2.7153
## 9 15.7517 nan 0.1000 2.0936
## 10 14.0820 nan 0.1000 1.5736
## 20 5.7109 nan 0.1000 0.2164
## 40 3.4308 nan 0.1000 -0.0207
## 60 2.8753 nan 0.1000 -0.0364
## 80 2.4971 nan 0.1000 -0.0164
## 100 2.2875 nan 0.1000 -0.0254
## 120 2.1093 nan 0.1000 -0.0271
## 140 1.9871 nan 0.1000 -0.0355
## 160 1.8746 nan 0.1000 -0.0181
## 180 1.7703 nan 0.1000 -0.0229
## 200 1.6605 nan 0.1000 -0.0157
## 220 1.5468 nan 0.1000 -0.0037
## 240 1.4602 nan 0.1000 -0.0220
## 260 1.3786 nan 0.1000 -0.0156
## 280 1.3131 nan 0.1000 -0.0282
## 300 1.2479 nan 0.1000 -0.0094
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.5432 nan 0.1000 8.9100
## 2 44.1516 nan 0.1000 7.9868
## 3 37.7469 nan 0.1000 6.9758
## 4 32.0433 nan 0.1000 5.1038
## 5 27.6694 nan 0.1000 4.1162
## 6 23.9474 nan 0.1000 3.7969
## 7 20.6435 nan 0.1000 2.9436
## 8 17.7614 nan 0.1000 2.3296
## 9 15.7301 nan 0.1000 2.2101
## 10 13.9316 nan 0.1000 1.6741
## 20 5.9425 nan 0.1000 0.2400
## 40 3.7335 nan 0.1000 -0.0056
## 60 3.1698 nan 0.1000 -0.0241
## 80 2.9054 nan 0.1000 -0.0358
## 100 2.6041 nan 0.1000 -0.0406
## 120 2.4028 nan 0.1000 -0.0254
## 140 2.2637 nan 0.1000 -0.0225
## 160 2.1652 nan 0.1000 -0.0313
## 180 2.0107 nan 0.1000 -0.0104
## 200 1.9042 nan 0.1000 -0.0235
## 220 1.8182 nan 0.1000 -0.0268
## 240 1.7075 nan 0.1000 -0.0145
## 260 1.6253 nan 0.1000 -0.0193
## 280 1.5627 nan 0.1000 -0.0164
## 300 1.4935 nan 0.1000 -0.0174
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.9336 nan 0.1000 10.3367
## 2 43.6281 nan 0.1000 8.0883
## 3 36.3628 nan 0.1000 7.6088
## 4 30.5856 nan 0.1000 5.5530
## 5 25.8329 nan 0.1000 4.8506
## 6 21.9819 nan 0.1000 3.9893
## 7 18.6486 nan 0.1000 2.6236
## 8 15.9182 nan 0.1000 2.6920
## 9 13.5781 nan 0.1000 2.1532
## 10 11.8844 nan 0.1000 1.5916
## 20 4.5107 nan 0.1000 0.3219
## 40 2.5758 nan 0.1000 -0.0362
## 60 2.0615 nan 0.1000 -0.0524
## 80 1.6985 nan 0.1000 -0.0403
## 100 1.4105 nan 0.1000 -0.0276
## 120 1.2082 nan 0.1000 -0.0308
## 140 1.0267 nan 0.1000 -0.0264
## 160 0.8917 nan 0.1000 -0.0163
## 180 0.7778 nan 0.1000 -0.0115
## 200 0.6890 nan 0.1000 -0.0143
## 220 0.6099 nan 0.1000 -0.0086
## 240 0.5326 nan 0.1000 -0.0082
## 260 0.4704 nan 0.1000 -0.0084
## 280 0.4184 nan 0.1000 -0.0099
## 300 0.3751 nan 0.1000 -0.0041
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.9032 nan 0.1000 11.0510
## 2 43.4474 nan 0.1000 6.7246
## 3 36.5066 nan 0.1000 6.7035
## 4 30.9613 nan 0.1000 5.5813
## 5 26.3727 nan 0.1000 4.8901
## 6 22.2448 nan 0.1000 3.7377
## 7 19.1221 nan 0.1000 2.8910
## 8 16.6911 nan 0.1000 2.3823
## 9 14.4348 nan 0.1000 1.9521
## 10 12.4688 nan 0.1000 1.6485
## 20 4.6909 nan 0.1000 0.2618
## 40 2.7556 nan 0.1000 -0.0361
## 60 2.2479 nan 0.1000 -0.0555
## 80 1.9144 nan 0.1000 -0.0355
## 100 1.6718 nan 0.1000 -0.0154
## 120 1.4829 nan 0.1000 -0.0251
## 140 1.3412 nan 0.1000 -0.0173
## 160 1.2034 nan 0.1000 -0.0212
## 180 1.1210 nan 0.1000 -0.0208
## 200 1.0161 nan 0.1000 -0.0135
## 220 0.9270 nan 0.1000 -0.0145
## 240 0.8585 nan 0.1000 -0.0190
## 260 0.8004 nan 0.1000 -0.0126
## 280 0.7373 nan 0.1000 -0.0092
## 300 0.6856 nan 0.1000 -0.0155
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7761 nan 0.1000 9.3610
## 2 42.7726 nan 0.1000 8.9823
## 3 35.9887 nan 0.1000 6.6535
## 4 30.5143 nan 0.1000 5.0268
## 5 25.9943 nan 0.1000 3.8989
## 6 22.1294 nan 0.1000 3.6441
## 7 18.8914 nan 0.1000 3.0980
## 8 16.2929 nan 0.1000 2.3696
## 9 14.1484 nan 0.1000 2.1980
## 10 12.4185 nan 0.1000 1.6913
## 20 4.7602 nan 0.1000 0.1792
## 40 2.9744 nan 0.1000 -0.0121
## 60 2.5793 nan 0.1000 -0.0458
## 80 2.2736 nan 0.1000 -0.0357
## 100 2.0470 nan 0.1000 -0.0203
## 120 1.8663 nan 0.1000 -0.0388
## 140 1.7058 nan 0.1000 -0.0197
## 160 1.5690 nan 0.1000 -0.0272
## 180 1.4434 nan 0.1000 -0.0092
## 200 1.3260 nan 0.1000 -0.0100
## 220 1.2618 nan 0.1000 -0.0237
## 240 1.1755 nan 0.1000 -0.0068
## 260 1.0905 nan 0.1000 -0.0111
## 280 1.0337 nan 0.1000 -0.0177
## 300 0.9815 nan 0.1000 -0.0159
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9985 nan 0.0100 0.7875
## 2 62.2174 nan 0.0100 0.8430
## 3 61.4593 nan 0.0100 0.7321
## 4 60.6916 nan 0.0100 0.7277
## 5 59.9171 nan 0.0100 0.6648
## 6 59.1093 nan 0.0100 0.7167
## 7 58.3689 nan 0.0100 0.7277
## 8 57.6622 nan 0.0100 0.7277
## 9 57.0034 nan 0.0100 0.6817
## 10 56.3371 nan 0.0100 0.6682
## 20 50.0316 nan 0.0100 0.5674
## 40 40.3489 nan 0.0100 0.4168
## 60 32.9237 nan 0.0100 0.2933
## 80 27.3044 nan 0.0100 0.2460
## 100 22.9947 nan 0.0100 0.1983
## 120 19.5744 nan 0.0100 0.1364
## 140 16.9284 nan 0.0100 0.0978
## 160 14.8381 nan 0.0100 0.0549
## 180 13.1188 nan 0.0100 0.0543
## 200 11.7126 nan 0.0100 0.0510
## 220 10.5697 nan 0.0100 0.0421
## 240 9.6024 nan 0.0100 0.0335
## 260 8.7650 nan 0.0100 0.0252
## 280 8.0587 nan 0.0100 0.0262
## 300 7.4808 nan 0.0100 0.0129
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.9728 nan 0.0100 0.8075
## 2 62.2199 nan 0.0100 0.6267
## 3 61.3663 nan 0.0100 0.8007
## 4 60.5435 nan 0.0100 0.7518
## 5 59.7788 nan 0.0100 0.7242
## 6 59.0246 nan 0.0100 0.7037
## 7 58.2770 nan 0.0100 0.6863
## 8 57.6106 nan 0.0100 0.6865
## 9 56.8931 nan 0.0100 0.7189
## 10 56.1347 nan 0.0100 0.6958
## 20 49.9446 nan 0.0100 0.5677
## 40 40.1068 nan 0.0100 0.3826
## 60 32.7367 nan 0.0100 0.3046
## 80 27.2530 nan 0.0100 0.1204
## 100 22.9359 nan 0.0100 0.1727
## 120 19.6452 nan 0.0100 0.1264
## 140 17.0291 nan 0.0100 0.0933
## 160 14.8957 nan 0.0100 0.0621
## 180 13.1704 nan 0.0100 0.0646
## 200 11.7834 nan 0.0100 0.0420
## 220 10.6054 nan 0.0100 0.0453
## 240 9.6261 nan 0.0100 0.0382
## 260 8.7814 nan 0.0100 0.0347
## 280 8.0555 nan 0.0100 0.0203
## 300 7.4630 nan 0.0100 0.0138
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 63.0177 nan 0.0100 0.7986
## 2 62.2306 nan 0.0100 0.7906
## 3 61.4139 nan 0.0100 0.7346
## 4 60.6331 nan 0.0100 0.7934
## 5 59.9006 nan 0.0100 0.7553
## 6 59.1494 nan 0.0100 0.7061
## 7 58.4641 nan 0.0100 0.7200
## 8 57.7236 nan 0.0100 0.6793
## 9 56.9957 nan 0.0100 0.6509
## 10 56.3227 nan 0.0100 0.6699
## 20 50.0636 nan 0.0100 0.4560
## 40 40.2629 nan 0.0100 0.3811
## 60 32.8189 nan 0.0100 0.2948
## 80 27.2182 nan 0.0100 0.2229
## 100 23.0342 nan 0.0100 0.1894
## 120 19.6412 nan 0.0100 0.1346
## 140 17.0362 nan 0.0100 0.1132
## 160 14.9164 nan 0.0100 0.0774
## 180 13.1875 nan 0.0100 0.0667
## 200 11.7668 nan 0.0100 0.0506
## 220 10.6012 nan 0.0100 0.0413
## 240 9.6379 nan 0.0100 0.0366
## 260 8.8481 nan 0.0100 0.0326
## 280 8.1773 nan 0.0100 0.0273
## 300 7.5830 nan 0.0100 0.0212
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.8472 nan 0.0100 1.0580
## 2 61.8151 nan 0.0100 1.0282
## 3 60.7652 nan 0.0100 0.9007
## 4 59.7377 nan 0.0100 0.9919
## 5 58.7778 nan 0.0100 0.9584
## 6 57.8416 nan 0.0100 0.9681
## 7 56.8664 nan 0.0100 0.9437
## 8 55.9463 nan 0.0100 1.0023
## 9 54.9986 nan 0.0100 0.8953
## 10 54.1560 nan 0.0100 0.8761
## 20 46.2454 nan 0.0100 0.8056
## 40 33.9783 nan 0.0100 0.5523
## 60 25.3631 nan 0.0100 0.2939
## 80 19.3733 nan 0.0100 0.2572
## 100 14.9972 nan 0.0100 0.1952
## 120 11.9489 nan 0.0100 0.1146
## 140 9.7948 nan 0.0100 0.0841
## 160 8.1664 nan 0.0100 0.0520
## 180 6.9351 nan 0.0100 0.0401
## 200 6.0076 nan 0.0100 0.0287
## 220 5.3065 nan 0.0100 0.0260
## 240 4.7662 nan 0.0100 0.0192
## 260 4.3874 nan 0.0100 0.0037
## 280 4.0867 nan 0.0100 0.0093
## 300 3.8419 nan 0.0100 0.0078
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.7955 nan 0.0100 1.1215
## 2 61.7435 nan 0.0100 0.9638
## 3 60.6800 nan 0.0100 1.0060
## 4 59.6701 nan 0.0100 0.9358
## 5 58.6875 nan 0.0100 0.9502
## 6 57.7191 nan 0.0100 0.9013
## 7 56.7564 nan 0.0100 0.8782
## 8 55.8102 nan 0.0100 1.0219
## 9 54.8753 nan 0.0100 0.7793
## 10 53.9803 nan 0.0100 0.7803
## 20 45.9157 nan 0.0100 0.7360
## 40 33.6841 nan 0.0100 0.4659
## 60 25.0701 nan 0.0100 0.3866
## 80 19.0979 nan 0.0100 0.2252
## 100 14.8930 nan 0.0100 0.1779
## 120 11.8803 nan 0.0100 0.1228
## 140 9.7416 nan 0.0100 0.0843
## 160 8.1806 nan 0.0100 0.0573
## 180 7.0130 nan 0.0100 0.0458
## 200 6.0820 nan 0.0100 0.0307
## 220 5.4204 nan 0.0100 0.0116
## 240 4.8834 nan 0.0100 0.0197
## 260 4.4943 nan 0.0100 0.0112
## 280 4.2012 nan 0.0100 0.0040
## 300 3.9609 nan 0.0100 0.0024
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.7610 nan 0.0100 1.1142
## 2 61.7814 nan 0.0100 1.0401
## 3 60.7940 nan 0.0100 0.9031
## 4 59.7810 nan 0.0100 0.9792
## 5 58.8219 nan 0.0100 0.9726
## 6 57.8467 nan 0.0100 1.0026
## 7 56.8668 nan 0.0100 0.8839
## 8 55.8837 nan 0.0100 0.8685
## 9 54.9786 nan 0.0100 0.9190
## 10 54.1008 nan 0.0100 0.9809
## 20 46.0141 nan 0.0100 0.7153
## 40 33.8735 nan 0.0100 0.4247
## 60 25.3138 nan 0.0100 0.3529
## 80 19.3380 nan 0.0100 0.2140
## 100 15.1335 nan 0.0100 0.1771
## 120 12.0413 nan 0.0100 0.1170
## 140 9.8881 nan 0.0100 0.0918
## 160 8.2730 nan 0.0100 0.0556
## 180 7.1074 nan 0.0100 0.0431
## 200 6.2181 nan 0.0100 0.0334
## 220 5.4841 nan 0.0100 0.0248
## 240 4.9866 nan 0.0100 0.0164
## 260 4.6185 nan 0.0100 0.0092
## 280 4.3113 nan 0.0100 0.0071
## 300 4.0936 nan 0.0100 0.0082
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.7480 nan 0.0100 0.9854
## 2 61.6227 nan 0.0100 1.1596
## 3 60.5054 nan 0.0100 1.2123
## 4 59.4158 nan 0.0100 0.9801
## 5 58.3669 nan 0.0100 0.9509
## 6 57.3524 nan 0.0100 1.0987
## 7 56.3448 nan 0.0100 1.1202
## 8 55.3708 nan 0.0100 0.7840
## 9 54.4041 nan 0.0100 0.9042
## 10 53.4696 nan 0.0100 0.8723
## 20 45.1079 nan 0.0100 0.7032
## 40 32.2431 nan 0.0100 0.5311
## 60 23.4316 nan 0.0100 0.3352
## 80 17.3572 nan 0.0100 0.2649
## 100 13.0953 nan 0.0100 0.1503
## 120 10.1238 nan 0.0100 0.1069
## 140 8.0787 nan 0.0100 0.0690
## 160 6.5862 nan 0.0100 0.0594
## 180 5.5409 nan 0.0100 0.0293
## 200 4.7941 nan 0.0100 0.0234
## 220 4.2283 nan 0.0100 0.0126
## 240 3.7993 nan 0.0100 0.0093
## 260 3.4848 nan 0.0100 0.0033
## 280 3.2269 nan 0.0100 0.0041
## 300 3.0365 nan 0.0100 -0.0025
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.7756 nan 0.0100 1.1535
## 2 61.6614 nan 0.0100 1.1594
## 3 60.5751 nan 0.0100 1.0897
## 4 59.4898 nan 0.0100 1.1126
## 5 58.4567 nan 0.0100 1.0362
## 6 57.4327 nan 0.0100 0.9636
## 7 56.3995 nan 0.0100 0.9951
## 8 55.3840 nan 0.0100 0.9755
## 9 54.4486 nan 0.0100 0.9077
## 10 53.5274 nan 0.0100 0.8308
## 20 45.1321 nan 0.0100 0.7084
## 40 32.4246 nan 0.0100 0.4667
## 60 23.5740 nan 0.0100 0.3287
## 80 17.5295 nan 0.0100 0.2455
## 100 13.2449 nan 0.0100 0.1571
## 120 10.3244 nan 0.0100 0.1158
## 140 8.2139 nan 0.0100 0.0646
## 160 6.7541 nan 0.0100 0.0378
## 180 5.7101 nan 0.0100 0.0431
## 200 4.9589 nan 0.0100 0.0234
## 220 4.4198 nan 0.0100 0.0154
## 240 4.0180 nan 0.0100 0.0119
## 260 3.7349 nan 0.0100 0.0071
## 280 3.4961 nan 0.0100 0.0057
## 300 3.3209 nan 0.0100 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 62.7386 nan 0.0100 1.1608
## 2 61.6411 nan 0.0100 1.0759
## 3 60.5095 nan 0.0100 1.0758
## 4 59.4845 nan 0.0100 0.9343
## 5 58.5064 nan 0.0100 1.0502
## 6 57.4671 nan 0.0100 0.9650
## 7 56.4945 nan 0.0100 1.0243
## 8 55.5046 nan 0.0100 0.9557
## 9 54.4976 nan 0.0100 0.8931
## 10 53.5467 nan 0.0100 0.7987
## 20 45.1999 nan 0.0100 0.7831
## 40 32.5786 nan 0.0100 0.5120
## 60 23.7932 nan 0.0100 0.3348
## 80 17.7073 nan 0.0100 0.2131
## 100 13.4632 nan 0.0100 0.1663
## 120 10.5338 nan 0.0100 0.1182
## 140 8.4930 nan 0.0100 0.0772
## 160 7.0194 nan 0.0100 0.0454
## 180 5.9687 nan 0.0100 0.0327
## 200 5.2346 nan 0.0100 0.0320
## 220 4.6896 nan 0.0100 0.0176
## 240 4.2815 nan 0.0100 0.0091
## 260 3.9894 nan 0.0100 0.0070
## 280 3.7589 nan 0.0100 -0.0015
## 300 3.5781 nan 0.0100 0.0057
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.9098 nan 0.0500 4.1048
## 2 56.4271 nan 0.0500 3.6368
## 3 53.0966 nan 0.0500 3.2035
## 4 50.1659 nan 0.0500 3.1309
## 5 47.5503 nan 0.0500 2.6222
## 6 44.7888 nan 0.0500 2.5692
## 7 42.6590 nan 0.0500 2.1013
## 8 40.5665 nan 0.0500 1.9516
## 9 38.4932 nan 0.0500 2.1868
## 10 36.4560 nan 0.0500 1.8213
## 20 23.4781 nan 0.0500 0.9012
## 40 11.8874 nan 0.0500 0.2211
## 60 7.5922 nan 0.0500 0.0636
## 80 5.7229 nan 0.0500 0.0263
## 100 4.7217 nan 0.0500 0.0176
## 120 4.2670 nan 0.0500 -0.0032
## 140 4.0540 nan 0.0500 -0.0170
## 160 3.9030 nan 0.0500 -0.0012
## 180 3.8103 nan 0.0500 -0.0150
## 200 3.7078 nan 0.0500 -0.0027
## 220 3.6273 nan 0.0500 -0.0044
## 240 3.5434 nan 0.0500 -0.0043
## 260 3.4911 nan 0.0500 -0.0070
## 280 3.4394 nan 0.0500 -0.0119
## 300 3.3906 nan 0.0500 -0.0059
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 59.6441 nan 0.0500 3.7437
## 2 56.0616 nan 0.0500 3.3833
## 3 52.4354 nan 0.0500 3.4142
## 4 49.6572 nan 0.0500 2.7917
## 5 46.9582 nan 0.0500 2.5597
## 6 44.6289 nan 0.0500 2.5130
## 7 42.1087 nan 0.0500 2.4419
## 8 39.9137 nan 0.0500 2.1136
## 9 37.8415 nan 0.0500 2.1188
## 10 35.9150 nan 0.0500 1.7569
## 20 22.4811 nan 0.0500 0.8767
## 40 11.5685 nan 0.0500 0.2598
## 60 7.3333 nan 0.0500 0.1086
## 80 5.4810 nan 0.0500 0.0296
## 100 4.6179 nan 0.0500 0.0190
## 120 4.1811 nan 0.0500 0.0026
## 140 3.9598 nan 0.0500 0.0009
## 160 3.8300 nan 0.0500 -0.0134
## 180 3.7330 nan 0.0500 -0.0015
## 200 3.6539 nan 0.0500 -0.0107
## 220 3.5875 nan 0.0500 -0.0024
## 240 3.5257 nan 0.0500 -0.0077
## 260 3.4681 nan 0.0500 -0.0058
## 280 3.4136 nan 0.0500 -0.0014
## 300 3.3698 nan 0.0500 -0.0084
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.0469 nan 0.0500 4.2440
## 2 56.7843 nan 0.0500 3.6193
## 3 53.5268 nan 0.0500 3.5454
## 4 50.3479 nan 0.0500 2.9587
## 5 47.2906 nan 0.0500 2.7413
## 6 44.6741 nan 0.0500 2.4363
## 7 42.3988 nan 0.0500 2.1610
## 8 40.1805 nan 0.0500 2.2103
## 9 38.0956 nan 0.0500 1.8684
## 10 36.1982 nan 0.0500 1.9085
## 20 22.8428 nan 0.0500 0.9003
## 40 11.7585 nan 0.0500 0.2078
## 60 7.5963 nan 0.0500 0.1208
## 80 5.6870 nan 0.0500 0.0073
## 100 4.8346 nan 0.0500 0.0062
## 120 4.4647 nan 0.0500 0.0001
## 140 4.2605 nan 0.0500 -0.0072
## 160 4.1150 nan 0.0500 0.0017
## 180 4.0121 nan 0.0500 -0.0009
## 200 3.9124 nan 0.0500 -0.0246
## 220 3.8312 nan 0.0500 0.0003
## 240 3.7587 nan 0.0500 -0.0031
## 260 3.6907 nan 0.0500 -0.0183
## 280 3.6319 nan 0.0500 -0.0143
## 300 3.5749 nan 0.0500 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.7636 nan 0.0500 5.1433
## 2 54.0719 nan 0.0500 4.1461
## 3 49.7294 nan 0.0500 4.0655
## 4 45.8498 nan 0.0500 4.0425
## 5 42.2136 nan 0.0500 3.4826
## 6 38.9837 nan 0.0500 3.1701
## 7 36.1836 nan 0.0500 2.4554
## 8 33.5145 nan 0.0500 2.7270
## 9 31.0261 nan 0.0500 2.6203
## 10 28.9104 nan 0.0500 2.2185
## 20 14.5809 nan 0.0500 0.9342
## 40 5.8341 nan 0.0500 0.1041
## 60 3.7838 nan 0.0500 0.0263
## 80 3.1607 nan 0.0500 0.0126
## 100 2.8608 nan 0.0500 -0.0172
## 120 2.6389 nan 0.0500 -0.0127
## 140 2.4352 nan 0.0500 -0.0089
## 160 2.2785 nan 0.0500 -0.0009
## 180 2.1598 nan 0.0500 -0.0006
## 200 2.0275 nan 0.0500 -0.0229
## 220 1.9335 nan 0.0500 -0.0097
## 240 1.8510 nan 0.0500 -0.0099
## 260 1.7582 nan 0.0500 -0.0070
## 280 1.6813 nan 0.0500 -0.0074
## 300 1.6014 nan 0.0500 -0.0134
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.5867 nan 0.0500 5.2170
## 2 54.0468 nan 0.0500 4.2882
## 3 49.8288 nan 0.0500 4.4252
## 4 45.9101 nan 0.0500 3.9567
## 5 42.3846 nan 0.0500 3.6024
## 6 39.3939 nan 0.0500 3.6098
## 7 36.4173 nan 0.0500 3.0475
## 8 33.7597 nan 0.0500 2.5965
## 9 31.1969 nan 0.0500 2.4442
## 10 28.8064 nan 0.0500 2.2393
## 20 14.7728 nan 0.0500 0.7685
## 40 5.9743 nan 0.0500 0.1615
## 60 4.0018 nan 0.0500 0.0378
## 80 3.3948 nan 0.0500 0.0056
## 100 3.0703 nan 0.0500 -0.0164
## 120 2.8332 nan 0.0500 -0.0131
## 140 2.6644 nan 0.0500 -0.0022
## 160 2.5310 nan 0.0500 -0.0085
## 180 2.3943 nan 0.0500 -0.0160
## 200 2.2851 nan 0.0500 -0.0026
## 220 2.1822 nan 0.0500 -0.0140
## 240 2.1083 nan 0.0500 -0.0077
## 260 2.0309 nan 0.0500 -0.0073
## 280 1.9559 nan 0.0500 -0.0088
## 300 1.8858 nan 0.0500 -0.0126
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.8172 nan 0.0500 5.0553
## 2 53.9460 nan 0.0500 4.9119
## 3 49.8320 nan 0.0500 4.3022
## 4 45.7921 nan 0.0500 4.1106
## 5 42.4456 nan 0.0500 3.3215
## 6 39.1334 nan 0.0500 2.8395
## 7 36.0692 nan 0.0500 3.2623
## 8 33.3416 nan 0.0500 2.5261
## 9 30.8669 nan 0.0500 2.3282
## 10 28.5887 nan 0.0500 2.2618
## 20 14.5897 nan 0.0500 0.8836
## 40 6.1577 nan 0.0500 0.1726
## 60 4.1437 nan 0.0500 0.0334
## 80 3.5131 nan 0.0500 0.0210
## 100 3.2283 nan 0.0500 -0.0141
## 120 2.9968 nan 0.0500 -0.0204
## 140 2.8371 nan 0.0500 -0.0056
## 160 2.6838 nan 0.0500 -0.0142
## 180 2.5663 nan 0.0500 -0.0203
## 200 2.4534 nan 0.0500 -0.0067
## 220 2.3746 nan 0.0500 -0.0091
## 240 2.2865 nan 0.0500 -0.0051
## 260 2.2246 nan 0.0500 -0.0157
## 280 2.1581 nan 0.0500 -0.0094
## 300 2.0965 nan 0.0500 -0.0149
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.3393 nan 0.0500 5.1045
## 2 53.2065 nan 0.0500 5.1345
## 3 48.5108 nan 0.0500 4.8570
## 4 44.3641 nan 0.0500 3.6656
## 5 40.9514 nan 0.0500 3.7283
## 6 37.5076 nan 0.0500 3.3830
## 7 34.4473 nan 0.0500 2.9459
## 8 31.6661 nan 0.0500 2.7402
## 9 29.1464 nan 0.0500 2.4814
## 10 26.8289 nan 0.0500 2.0719
## 20 12.9020 nan 0.0500 0.7695
## 40 4.8440 nan 0.0500 0.0998
## 60 3.0419 nan 0.0500 0.0075
## 80 2.5065 nan 0.0500 -0.0242
## 100 2.1707 nan 0.0500 -0.0107
## 120 1.9493 nan 0.0500 -0.0155
## 140 1.7679 nan 0.0500 -0.0155
## 160 1.5994 nan 0.0500 -0.0232
## 180 1.4622 nan 0.0500 -0.0177
## 200 1.3392 nan 0.0500 -0.0166
## 220 1.2215 nan 0.0500 -0.0102
## 240 1.1252 nan 0.0500 -0.0116
## 260 1.0387 nan 0.0500 -0.0144
## 280 0.9759 nan 0.0500 -0.0087
## 300 0.9170 nan 0.0500 -0.0085
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.0569 nan 0.0500 5.8005
## 2 53.2120 nan 0.0500 4.3338
## 3 48.5908 nan 0.0500 4.7339
## 4 44.5846 nan 0.0500 3.8315
## 5 41.0023 nan 0.0500 3.6521
## 6 37.7011 nan 0.0500 3.4177
## 7 34.5523 nan 0.0500 2.4721
## 8 31.7436 nan 0.0500 2.5796
## 9 29.2161 nan 0.0500 2.5167
## 10 27.0388 nan 0.0500 2.0536
## 20 13.1026 nan 0.0500 0.7898
## 40 4.7899 nan 0.0500 0.1368
## 60 3.1979 nan 0.0500 -0.0115
## 80 2.7368 nan 0.0500 0.0016
## 100 2.4480 nan 0.0500 -0.0069
## 120 2.2418 nan 0.0500 -0.0086
## 140 2.0646 nan 0.0500 -0.0132
## 160 1.9299 nan 0.0500 -0.0106
## 180 1.7936 nan 0.0500 -0.0151
## 200 1.6881 nan 0.0500 -0.0099
## 220 1.5813 nan 0.0500 -0.0165
## 240 1.4927 nan 0.0500 -0.0082
## 260 1.4125 nan 0.0500 -0.0106
## 280 1.3407 nan 0.0500 -0.0105
## 300 1.2748 nan 0.0500 -0.0063
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 58.5613 nan 0.0500 5.1229
## 2 53.7997 nan 0.0500 5.4120
## 3 49.4664 nan 0.0500 4.2559
## 4 45.2781 nan 0.0500 4.2120
## 5 41.3838 nan 0.0500 3.5533
## 6 37.9544 nan 0.0500 3.2361
## 7 34.8002 nan 0.0500 2.7159
## 8 31.8574 nan 0.0500 2.7398
## 9 29.3410 nan 0.0500 2.5587
## 10 27.1484 nan 0.0500 2.0509
## 20 13.0973 nan 0.0500 0.8598
## 40 5.1876 nan 0.0500 0.1188
## 60 3.6646 nan 0.0500 0.0025
## 80 3.1571 nan 0.0500 -0.0155
## 100 2.8687 nan 0.0500 -0.0219
## 120 2.6294 nan 0.0500 -0.0223
## 140 2.4278 nan 0.0500 -0.0298
## 160 2.2907 nan 0.0500 -0.0109
## 180 2.1632 nan 0.0500 -0.0107
## 200 2.0605 nan 0.0500 -0.0187
## 220 1.9530 nan 0.0500 -0.0143
## 240 1.8823 nan 0.0500 -0.0125
## 260 1.8020 nan 0.0500 -0.0163
## 280 1.7263 nan 0.0500 -0.0136
## 300 1.6653 nan 0.0500 -0.0064
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.7107 nan 0.1000 7.1249
## 2 49.9424 nan 0.1000 6.8176
## 3 44.9423 nan 0.1000 5.4342
## 4 40.2198 nan 0.1000 4.5277
## 5 36.0712 nan 0.1000 3.3928
## 6 32.6024 nan 0.1000 3.4584
## 7 29.5447 nan 0.1000 2.7444
## 8 26.9974 nan 0.1000 2.4453
## 9 24.6215 nan 0.1000 2.3974
## 10 22.6000 nan 0.1000 1.5529
## 20 11.6752 nan 0.1000 0.4538
## 40 5.7940 nan 0.1000 0.0529
## 60 4.4432 nan 0.1000 0.0219
## 80 4.0681 nan 0.1000 0.0001
## 100 3.8773 nan 0.1000 -0.0022
## 120 3.6893 nan 0.1000 -0.0028
## 140 3.5515 nan 0.1000 -0.0605
## 160 3.4202 nan 0.1000 -0.0120
## 180 3.3227 nan 0.1000 -0.0218
## 200 3.2451 nan 0.1000 -0.0010
## 220 3.1666 nan 0.1000 -0.0259
## 240 3.0957 nan 0.1000 -0.0099
## 260 3.0286 nan 0.1000 -0.0066
## 280 2.9555 nan 0.1000 -0.0211
## 300 2.9066 nan 0.1000 -0.0321
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 55.7692 nan 0.1000 7.9460
## 2 49.4909 nan 0.1000 6.1097
## 3 44.2695 nan 0.1000 4.8559
## 4 40.0814 nan 0.1000 3.7968
## 5 36.3905 nan 0.1000 3.8122
## 6 32.8987 nan 0.1000 3.6521
## 7 29.7813 nan 0.1000 2.9491
## 8 27.0564 nan 0.1000 2.7733
## 9 24.4975 nan 0.1000 2.4207
## 10 22.4945 nan 0.1000 1.7237
## 20 11.7907 nan 0.1000 0.4792
## 40 5.7844 nan 0.1000 0.1214
## 60 4.3809 nan 0.1000 0.0073
## 80 4.0432 nan 0.1000 -0.0040
## 100 3.8185 nan 0.1000 -0.0221
## 120 3.6507 nan 0.1000 -0.0031
## 140 3.5547 nan 0.1000 -0.0223
## 160 3.4571 nan 0.1000 -0.0153
## 180 3.3754 nan 0.1000 -0.0173
## 200 3.3178 nan 0.1000 -0.0201
## 220 3.2456 nan 0.1000 -0.0263
## 240 3.1805 nan 0.1000 -0.0181
## 260 3.1171 nan 0.1000 -0.0124
## 280 3.0690 nan 0.1000 -0.0162
## 300 3.0136 nan 0.1000 -0.0227
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.6537 nan 0.1000 7.8991
## 2 50.3931 nan 0.1000 6.2872
## 3 45.0122 nan 0.1000 5.3608
## 4 40.9096 nan 0.1000 3.8877
## 5 36.7605 nan 0.1000 3.7550
## 6 33.2573 nan 0.1000 3.3243
## 7 29.9867 nan 0.1000 2.8839
## 8 27.3614 nan 0.1000 2.2408
## 9 24.9204 nan 0.1000 2.2853
## 10 23.1194 nan 0.1000 1.6966
## 20 11.9908 nan 0.1000 0.5310
## 40 5.8346 nan 0.1000 0.1196
## 60 4.6497 nan 0.1000 -0.0195
## 80 4.2677 nan 0.1000 -0.0409
## 100 4.0744 nan 0.1000 -0.0186
## 120 3.8959 nan 0.1000 -0.0138
## 140 3.7453 nan 0.1000 -0.0338
## 160 3.6214 nan 0.1000 -0.0071
## 180 3.5386 nan 0.1000 -0.0160
## 200 3.4588 nan 0.1000 -0.0171
## 220 3.4076 nan 0.1000 -0.0253
## 240 3.3565 nan 0.1000 -0.0274
## 260 3.2951 nan 0.1000 -0.0206
## 280 3.2426 nan 0.1000 -0.0497
## 300 3.1803 nan 0.1000 -0.0058
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.7873 nan 0.1000 9.1290
## 2 45.7962 nan 0.1000 7.7137
## 3 38.7359 nan 0.1000 7.1249
## 4 32.9892 nan 0.1000 5.7700
## 5 28.4315 nan 0.1000 4.3602
## 6 24.5868 nan 0.1000 3.5410
## 7 21.3789 nan 0.1000 3.1941
## 8 18.4510 nan 0.1000 2.6498
## 9 16.3676 nan 0.1000 2.1125
## 10 14.3417 nan 0.1000 1.7946
## 20 5.8491 nan 0.1000 0.3472
## 40 3.2467 nan 0.1000 0.0056
## 60 2.6828 nan 0.1000 -0.0118
## 80 2.2998 nan 0.1000 -0.0206
## 100 2.0345 nan 0.1000 -0.0233
## 120 1.8409 nan 0.1000 -0.0126
## 140 1.6791 nan 0.1000 -0.0163
## 160 1.5443 nan 0.1000 -0.0178
## 180 1.4157 nan 0.1000 -0.0160
## 200 1.3092 nan 0.1000 -0.0176
## 220 1.2289 nan 0.1000 -0.0090
## 240 1.1451 nan 0.1000 -0.0043
## 260 1.0615 nan 0.1000 -0.0227
## 280 0.9799 nan 0.1000 -0.0122
## 300 0.9200 nan 0.1000 -0.0091
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.7308 nan 0.1000 10.1487
## 2 45.2547 nan 0.1000 8.7644
## 3 38.0509 nan 0.1000 7.3119
## 4 32.2444 nan 0.1000 5.4438
## 5 27.5389 nan 0.1000 4.3646
## 6 23.6937 nan 0.1000 3.3059
## 7 20.4775 nan 0.1000 2.9580
## 8 18.0230 nan 0.1000 2.5625
## 9 15.7928 nan 0.1000 2.2526
## 10 13.9186 nan 0.1000 1.7400
## 20 6.0184 nan 0.1000 0.2870
## 40 3.4837 nan 0.1000 -0.0186
## 60 2.8838 nan 0.1000 -0.0242
## 80 2.5470 nan 0.1000 -0.0324
## 100 2.2953 nan 0.1000 -0.0206
## 120 2.1178 nan 0.1000 -0.0265
## 140 1.9513 nan 0.1000 -0.0276
## 160 1.8246 nan 0.1000 -0.0446
## 180 1.7260 nan 0.1000 -0.0257
## 200 1.6415 nan 0.1000 -0.0203
## 220 1.5431 nan 0.1000 -0.0176
## 240 1.4581 nan 0.1000 -0.0107
## 260 1.3837 nan 0.1000 0.0000
## 280 1.3080 nan 0.1000 -0.0184
## 300 1.2463 nan 0.1000 -0.0138
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.3594 nan 0.1000 9.9251
## 2 44.5474 nan 0.1000 8.3285
## 3 37.8864 nan 0.1000 7.0288
## 4 32.1471 nan 0.1000 5.0124
## 5 27.7939 nan 0.1000 4.6844
## 6 23.9038 nan 0.1000 3.7461
## 7 20.8659 nan 0.1000 3.1656
## 8 18.3206 nan 0.1000 2.2065
## 9 15.8902 nan 0.1000 1.7098
## 10 13.9527 nan 0.1000 1.4113
## 20 6.0328 nan 0.1000 0.3251
## 40 3.4588 nan 0.1000 0.0191
## 60 2.9379 nan 0.1000 -0.0088
## 80 2.6484 nan 0.1000 -0.0270
## 100 2.4853 nan 0.1000 -0.0013
## 120 2.3029 nan 0.1000 -0.0151
## 140 2.1725 nan 0.1000 -0.0332
## 160 2.0444 nan 0.1000 -0.0456
## 180 1.9345 nan 0.1000 -0.0239
## 200 1.8337 nan 0.1000 -0.0175
## 220 1.7398 nan 0.1000 -0.0212
## 240 1.6474 nan 0.1000 -0.0359
## 260 1.5777 nan 0.1000 -0.0213
## 280 1.5068 nan 0.1000 -0.0181
## 300 1.4430 nan 0.1000 -0.0170
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.0479 nan 0.1000 9.8548
## 2 44.5334 nan 0.1000 7.5746
## 3 36.9696 nan 0.1000 7.1489
## 4 31.1362 nan 0.1000 4.9150
## 5 26.3586 nan 0.1000 4.7039
## 6 22.3089 nan 0.1000 3.8827
## 7 19.0925 nan 0.1000 2.8275
## 8 16.4445 nan 0.1000 2.6879
## 9 14.2281 nan 0.1000 2.2221
## 10 12.5914 nan 0.1000 1.6502
## 20 4.7730 nan 0.1000 0.2575
## 40 2.6609 nan 0.1000 -0.0291
## 60 2.0643 nan 0.1000 -0.0378
## 80 1.7014 nan 0.1000 -0.0381
## 100 1.4711 nan 0.1000 -0.0197
## 120 1.2311 nan 0.1000 -0.0223
## 140 1.0535 nan 0.1000 -0.0255
## 160 0.9040 nan 0.1000 -0.0086
## 180 0.7878 nan 0.1000 -0.0129
## 200 0.6791 nan 0.1000 -0.0121
## 220 0.6108 nan 0.1000 -0.0088
## 240 0.5407 nan 0.1000 -0.0070
## 260 0.4872 nan 0.1000 -0.0087
## 280 0.4424 nan 0.1000 -0.0106
## 300 0.3954 nan 0.1000 -0.0063
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.3589 nan 0.1000 9.8892
## 2 44.4164 nan 0.1000 8.4572
## 3 37.4332 nan 0.1000 6.8001
## 4 31.4089 nan 0.1000 6.7308
## 5 26.4813 nan 0.1000 4.5053
## 6 22.7440 nan 0.1000 3.4369
## 7 19.6367 nan 0.1000 2.9010
## 8 16.8183 nan 0.1000 2.8924
## 9 14.5057 nan 0.1000 2.3272
## 10 12.5532 nan 0.1000 1.7684
## 20 4.8839 nan 0.1000 0.2116
## 40 2.9095 nan 0.1000 -0.0104
## 60 2.3911 nan 0.1000 -0.0290
## 80 2.0573 nan 0.1000 -0.0123
## 100 1.8176 nan 0.1000 -0.0357
## 120 1.6304 nan 0.1000 -0.0341
## 140 1.4759 nan 0.1000 -0.0242
## 160 1.3384 nan 0.1000 -0.0333
## 180 1.1946 nan 0.1000 -0.0193
## 200 1.0783 nan 0.1000 -0.0194
## 220 0.9832 nan 0.1000 -0.0214
## 240 0.9016 nan 0.1000 -0.0080
## 260 0.8464 nan 0.1000 -0.0216
## 280 0.7652 nan 0.1000 -0.0049
## 300 0.7142 nan 0.1000 -0.0139
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.8167 nan 0.1000 10.5142
## 2 44.0910 nan 0.1000 8.7777
## 3 36.7865 nan 0.1000 7.6310
## 4 30.8366 nan 0.1000 5.4251
## 5 26.1056 nan 0.1000 4.5183
## 6 22.1096 nan 0.1000 4.1077
## 7 18.8688 nan 0.1000 2.8525
## 8 16.2652 nan 0.1000 2.5781
## 9 14.1881 nan 0.1000 1.7926
## 10 12.6016 nan 0.1000 1.5399
## 20 5.1494 nan 0.1000 0.2840
## 40 3.1300 nan 0.1000 0.0286
## 60 2.6763 nan 0.1000 -0.0292
## 80 2.3678 nan 0.1000 -0.0172
## 100 2.1244 nan 0.1000 -0.0044
## 120 1.9211 nan 0.1000 -0.0059
## 140 1.7671 nan 0.1000 -0.0238
## 160 1.6278 nan 0.1000 -0.0343
## 180 1.5020 nan 0.1000 -0.0219
## 200 1.3941 nan 0.1000 -0.0026
## 220 1.3018 nan 0.1000 -0.0218
## 240 1.2041 nan 0.1000 -0.0102
## 260 1.1194 nan 0.1000 -0.0223
## 280 1.0550 nan 0.1000 -0.0132
## 300 0.9905 nan 0.1000 -0.0189
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6111 nan 0.0100 0.7336
## 2 59.9035 nan 0.0100 0.7284
## 3 59.1369 nan 0.0100 0.6839
## 4 58.4266 nan 0.0100 0.7272
## 5 57.7643 nan 0.0100 0.6231
## 6 57.0606 nan 0.0100 0.6825
## 7 56.3533 nan 0.0100 0.6824
## 8 55.7011 nan 0.0100 0.6267
## 9 55.0193 nan 0.0100 0.6522
## 10 54.2865 nan 0.0100 0.6374
## 20 48.3009 nan 0.0100 0.5510
## 40 38.8594 nan 0.0100 0.3557
## 60 32.0326 nan 0.0100 0.2656
## 80 26.5393 nan 0.0100 0.2287
## 100 22.4845 nan 0.0100 0.1519
## 120 19.1429 nan 0.0100 0.1216
## 140 16.6190 nan 0.0100 0.1101
## 160 14.5774 nan 0.0100 0.0796
## 180 12.9194 nan 0.0100 0.0660
## 200 11.5409 nan 0.0100 0.0456
## 220 10.3951 nan 0.0100 0.0489
## 240 9.4656 nan 0.0100 0.0342
## 260 8.6582 nan 0.0100 0.0327
## 280 7.9663 nan 0.0100 0.0168
## 300 7.3633 nan 0.0100 0.0260
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6813 nan 0.0100 0.7310
## 2 59.9931 nan 0.0100 0.7730
## 3 59.2090 nan 0.0100 0.7390
## 4 58.4632 nan 0.0100 0.7210
## 5 57.6812 nan 0.0100 0.7051
## 6 56.9632 nan 0.0100 0.7568
## 7 56.2857 nan 0.0100 0.7171
## 8 55.6027 nan 0.0100 0.6815
## 9 55.0002 nan 0.0100 0.6361
## 10 54.3810 nan 0.0100 0.6466
## 20 48.4453 nan 0.0100 0.5504
## 40 38.9750 nan 0.0100 0.3946
## 60 32.0457 nan 0.0100 0.3000
## 80 26.6680 nan 0.0100 0.1879
## 100 22.5329 nan 0.0100 0.1950
## 120 19.3149 nan 0.0100 0.1066
## 140 16.7343 nan 0.0100 0.1035
## 160 14.6880 nan 0.0100 0.1041
## 180 12.9930 nan 0.0100 0.0655
## 200 11.6174 nan 0.0100 0.0573
## 220 10.5061 nan 0.0100 0.0399
## 240 9.5584 nan 0.0100 0.0358
## 260 8.7304 nan 0.0100 0.0296
## 280 8.0585 nan 0.0100 0.0154
## 300 7.4698 nan 0.0100 0.0220
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.6210 nan 0.0100 0.7643
## 2 59.9189 nan 0.0100 0.7546
## 3 59.2374 nan 0.0100 0.7288
## 4 58.4934 nan 0.0100 0.7229
## 5 57.7318 nan 0.0100 0.7413
## 6 57.0263 nan 0.0100 0.7308
## 7 56.3427 nan 0.0100 0.6451
## 8 55.6939 nan 0.0100 0.7285
## 9 55.0414 nan 0.0100 0.6567
## 10 54.3798 nan 0.0100 0.6393
## 20 48.3319 nan 0.0100 0.5471
## 40 38.8705 nan 0.0100 0.3552
## 60 31.8643 nan 0.0100 0.2721
## 80 26.5174 nan 0.0100 0.2332
## 100 22.4726 nan 0.0100 0.1578
## 120 19.1952 nan 0.0100 0.1279
## 140 16.7255 nan 0.0100 0.0919
## 160 14.6642 nan 0.0100 0.0679
## 180 13.0349 nan 0.0100 0.0511
## 200 11.7320 nan 0.0100 0.0558
## 220 10.6177 nan 0.0100 0.0514
## 240 9.6862 nan 0.0100 0.0366
## 260 8.9201 nan 0.0100 0.0354
## 280 8.2260 nan 0.0100 0.0205
## 300 7.6659 nan 0.0100 0.0220
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3664 nan 0.0100 0.9490
## 2 59.4029 nan 0.0100 0.9511
## 3 58.4293 nan 0.0100 0.9291
## 4 57.4619 nan 0.0100 1.0679
## 5 56.4938 nan 0.0100 0.9267
## 6 55.5583 nan 0.0100 0.8971
## 7 54.5650 nan 0.0100 1.0088
## 8 53.6148 nan 0.0100 0.7895
## 9 52.7555 nan 0.0100 0.8632
## 10 51.9086 nan 0.0100 0.7475
## 20 44.1231 nan 0.0100 0.6960
## 40 32.2861 nan 0.0100 0.4852
## 60 24.1975 nan 0.0100 0.3348
## 80 18.4346 nan 0.0100 0.2284
## 100 14.3806 nan 0.0100 0.1761
## 120 11.5370 nan 0.0100 0.1153
## 140 9.4902 nan 0.0100 0.0971
## 160 7.9619 nan 0.0100 0.0696
## 180 6.8200 nan 0.0100 0.0453
## 200 5.9702 nan 0.0100 0.0279
## 220 5.3070 nan 0.0100 0.0144
## 240 4.7880 nan 0.0100 0.0158
## 260 4.3930 nan 0.0100 0.0112
## 280 4.1102 nan 0.0100 0.0004
## 300 3.8631 nan 0.0100 0.0028
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3441 nan 0.0100 1.0884
## 2 59.3898 nan 0.0100 0.9820
## 3 58.4082 nan 0.0100 0.8843
## 4 57.4675 nan 0.0100 0.9217
## 5 56.5249 nan 0.0100 0.8988
## 6 55.6565 nan 0.0100 0.8703
## 7 54.6882 nan 0.0100 1.0201
## 8 53.7920 nan 0.0100 0.9010
## 9 52.8560 nan 0.0100 0.8745
## 10 51.9757 nan 0.0100 0.8403
## 20 44.1865 nan 0.0100 0.7569
## 40 32.4272 nan 0.0100 0.5145
## 60 24.2446 nan 0.0100 0.3711
## 80 18.4656 nan 0.0100 0.2337
## 100 14.5886 nan 0.0100 0.1065
## 120 11.7090 nan 0.0100 0.1212
## 140 9.6358 nan 0.0100 0.0848
## 160 8.0676 nan 0.0100 0.0557
## 180 6.9297 nan 0.0100 0.0340
## 200 6.0792 nan 0.0100 0.0228
## 220 5.4162 nan 0.0100 0.0264
## 240 4.9316 nan 0.0100 0.0122
## 260 4.5482 nan 0.0100 0.0079
## 280 4.2537 nan 0.0100 0.0042
## 300 4.0187 nan 0.0100 0.0046
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3500 nan 0.0100 1.0190
## 2 59.3603 nan 0.0100 0.9367
## 3 58.4078 nan 0.0100 0.8853
## 4 57.3704 nan 0.0100 0.9471
## 5 56.4431 nan 0.0100 0.9108
## 6 55.5651 nan 0.0100 0.8060
## 7 54.6937 nan 0.0100 0.9691
## 8 53.7700 nan 0.0100 0.9101
## 9 52.8925 nan 0.0100 0.8669
## 10 52.0657 nan 0.0100 0.8167
## 20 44.3176 nan 0.0100 0.7018
## 40 32.4732 nan 0.0100 0.4926
## 60 24.3577 nan 0.0100 0.3101
## 80 18.7292 nan 0.0100 0.2253
## 100 14.7540 nan 0.0100 0.1218
## 120 11.9618 nan 0.0100 0.1111
## 140 9.8608 nan 0.0100 0.0783
## 160 8.3147 nan 0.0100 0.0568
## 180 7.1793 nan 0.0100 0.0443
## 200 6.2955 nan 0.0100 0.0220
## 220 5.6613 nan 0.0100 0.0207
## 240 5.1533 nan 0.0100 0.0164
## 260 4.7771 nan 0.0100 0.0100
## 280 4.4948 nan 0.0100 0.0078
## 300 4.2677 nan 0.0100 0.0028
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.2489 nan 0.0100 1.1364
## 2 59.1998 nan 0.0100 1.0307
## 3 58.0793 nan 0.0100 1.1226
## 4 57.0762 nan 0.0100 0.8950
## 5 56.0592 nan 0.0100 1.0230
## 6 55.0295 nan 0.0100 1.0202
## 7 54.1311 nan 0.0100 0.9347
## 8 53.2610 nan 0.0100 0.8762
## 9 52.3056 nan 0.0100 0.8626
## 10 51.3837 nan 0.0100 0.8446
## 20 43.2125 nan 0.0100 0.7242
## 40 31.1719 nan 0.0100 0.4753
## 60 22.8254 nan 0.0100 0.2939
## 80 17.0122 nan 0.0100 0.2435
## 100 13.0349 nan 0.0100 0.1575
## 120 10.1533 nan 0.0100 0.1000
## 140 8.1657 nan 0.0100 0.0674
## 160 6.6953 nan 0.0100 0.0468
## 180 5.6460 nan 0.0100 0.0283
## 200 4.8689 nan 0.0100 0.0279
## 220 4.2930 nan 0.0100 0.0172
## 240 3.8906 nan 0.0100 0.0077
## 260 3.5652 nan 0.0100 0.0043
## 280 3.3217 nan 0.0100 0.0044
## 300 3.1260 nan 0.0100 0.0025
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.2968 nan 0.0100 1.1529
## 2 59.2411 nan 0.0100 1.0693
## 3 58.2234 nan 0.0100 0.9467
## 4 57.1841 nan 0.0100 0.9436
## 5 56.2098 nan 0.0100 0.9415
## 6 55.2144 nan 0.0100 0.9335
## 7 54.3086 nan 0.0100 0.9898
## 8 53.3484 nan 0.0100 0.9354
## 9 52.4844 nan 0.0100 0.9183
## 10 51.5769 nan 0.0100 0.9541
## 20 43.3390 nan 0.0100 0.7148
## 40 31.0169 nan 0.0100 0.5128
## 60 22.7752 nan 0.0100 0.3130
## 80 17.0462 nan 0.0100 0.2339
## 100 13.0226 nan 0.0100 0.1655
## 120 10.1888 nan 0.0100 0.0911
## 140 8.1973 nan 0.0100 0.0754
## 160 6.7405 nan 0.0100 0.0550
## 180 5.7111 nan 0.0100 0.0302
## 200 4.9774 nan 0.0100 0.0258
## 220 4.4524 nan 0.0100 0.0058
## 240 4.0628 nan 0.0100 0.0133
## 260 3.7658 nan 0.0100 0.0100
## 280 3.5400 nan 0.0100 0.0055
## 300 3.3572 nan 0.0100 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 60.3088 nan 0.0100 0.9468
## 2 59.2464 nan 0.0100 1.0670
## 3 58.2532 nan 0.0100 0.9555
## 4 57.2127 nan 0.0100 0.9789
## 5 56.2379 nan 0.0100 0.8822
## 6 55.2641 nan 0.0100 1.0158
## 7 54.3385 nan 0.0100 0.9156
## 8 53.4357 nan 0.0100 0.8923
## 9 52.5017 nan 0.0100 0.9272
## 10 51.6417 nan 0.0100 0.8443
## 20 43.5650 nan 0.0100 0.7546
## 40 31.5142 nan 0.0100 0.4929
## 60 23.1920 nan 0.0100 0.3233
## 80 17.4470 nan 0.0100 0.1912
## 100 13.4063 nan 0.0100 0.1669
## 120 10.5748 nan 0.0100 0.1018
## 140 8.5803 nan 0.0100 0.0700
## 160 7.1628 nan 0.0100 0.0424
## 180 6.1542 nan 0.0100 0.0324
## 200 5.4242 nan 0.0100 0.0271
## 220 4.8959 nan 0.0100 0.0172
## 240 4.5136 nan 0.0100 0.0071
## 260 4.2139 nan 0.0100 0.0060
## 280 3.9963 nan 0.0100 -0.0015
## 300 3.8197 nan 0.0100 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7044 nan 0.0500 3.6068
## 2 54.4860 nan 0.0500 3.6038
## 3 51.4795 nan 0.0500 3.0725
## 4 48.2829 nan 0.0500 2.7477
## 5 45.6534 nan 0.0500 2.4187
## 6 43.2131 nan 0.0500 2.4606
## 7 41.1646 nan 0.0500 2.1205
## 8 38.8947 nan 0.0500 2.1821
## 9 36.9404 nan 0.0500 1.8426
## 10 35.1515 nan 0.0500 1.5676
## 20 22.1450 nan 0.0500 0.8063
## 40 11.6054 nan 0.0500 0.2089
## 60 7.5295 nan 0.0500 0.0846
## 80 5.5828 nan 0.0500 0.0200
## 100 4.7260 nan 0.0500 0.0165
## 120 4.3173 nan 0.0500 0.0070
## 140 4.0960 nan 0.0500 -0.0066
## 160 3.9644 nan 0.0500 -0.0041
## 180 3.8590 nan 0.0500 -0.0016
## 200 3.7709 nan 0.0500 -0.0088
## 220 3.7133 nan 0.0500 -0.0113
## 240 3.6451 nan 0.0500 -0.0130
## 260 3.5943 nan 0.0500 -0.0192
## 280 3.5529 nan 0.0500 -0.0192
## 300 3.5120 nan 0.0500 -0.0054
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.7122 nan 0.0500 3.5301
## 2 54.1298 nan 0.0500 3.4151
## 3 51.0843 nan 0.0500 3.1384
## 4 48.2779 nan 0.0500 2.8377
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## 6 43.3251 nan 0.0500 2.3360
## 7 41.0335 nan 0.0500 2.3503
## 8 38.8456 nan 0.0500 2.0521
## 9 36.9219 nan 0.0500 1.8290
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## 20 22.3268 nan 0.0500 0.9230
## 40 11.4470 nan 0.0500 0.2404
## 60 7.4390 nan 0.0500 0.1025
## 80 5.6278 nan 0.0500 0.0516
## 100 4.7574 nan 0.0500 0.0200
## 120 4.3662 nan 0.0500 -0.0082
## 140 4.1667 nan 0.0500 -0.0029
## 160 4.0477 nan 0.0500 0.0005
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## 220 3.7828 nan 0.0500 -0.0028
## 240 3.7262 nan 0.0500 -0.0055
## 260 3.6650 nan 0.0500 -0.0101
## 280 3.6227 nan 0.0500 -0.0072
## 300 3.5902 nan 0.0500 -0.0080
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 57.5812 nan 0.0500 3.8763
## 2 54.3611 nan 0.0500 3.3412
## 3 51.1497 nan 0.0500 3.0675
## 4 48.3818 nan 0.0500 3.0605
## 5 45.7192 nan 0.0500 2.6437
## 6 43.3694 nan 0.0500 2.3816
## 7 40.9379 nan 0.0500 2.3437
## 8 38.8622 nan 0.0500 2.0155
## 9 36.8831 nan 0.0500 1.6641
## 10 35.2245 nan 0.0500 1.7334
## 20 22.5093 nan 0.0500 0.8259
## 40 11.6596 nan 0.0500 0.3277
## 60 7.6610 nan 0.0500 0.0858
## 80 5.8494 nan 0.0500 0.0685
## 100 4.9810 nan 0.0500 0.0095
## 120 4.6103 nan 0.0500 0.0128
## 140 4.4076 nan 0.0500 0.0105
## 160 4.2791 nan 0.0500 -0.0190
## 180 4.1837 nan 0.0500 -0.0064
## 200 4.0930 nan 0.0500 0.0001
## 220 4.0244 nan 0.0500 -0.0168
## 240 3.9636 nan 0.0500 -0.0074
## 260 3.8951 nan 0.0500 -0.0096
## 280 3.8323 nan 0.0500 -0.0074
## 300 3.7825 nan 0.0500 -0.0038
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.2194 nan 0.0500 4.6312
## 2 51.9347 nan 0.0500 4.9296
## 3 48.0273 nan 0.0500 3.7486
## 4 44.2328 nan 0.0500 4.1860
## 5 40.7202 nan 0.0500 3.5817
## 6 37.6699 nan 0.0500 3.2747
## 7 34.9171 nan 0.0500 2.4521
## 8 32.3180 nan 0.0500 2.6075
## 9 29.9564 nan 0.0500 2.0336
## 10 27.7736 nan 0.0500 2.2388
## 20 14.2743 nan 0.0500 0.7548
## 40 6.0720 nan 0.0500 0.1678
## 60 3.9432 nan 0.0500 0.0556
## 80 3.3035 nan 0.0500 -0.0064
## 100 2.9980 nan 0.0500 -0.0194
## 120 2.7428 nan 0.0500 -0.0093
## 140 2.5584 nan 0.0500 -0.0061
## 160 2.4108 nan 0.0500 -0.0224
## 180 2.2766 nan 0.0500 0.0003
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## 220 2.0407 nan 0.0500 -0.0073
## 240 1.9323 nan 0.0500 -0.0095
## 260 1.8425 nan 0.0500 -0.0126
## 280 1.7615 nan 0.0500 -0.0163
## 300 1.6895 nan 0.0500 -0.0048
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.3794 nan 0.0500 5.0040
## 2 51.8961 nan 0.0500 4.4267
## 3 47.6231 nan 0.0500 3.5971
## 4 43.7211 nan 0.0500 3.5671
## 5 40.1669 nan 0.0500 3.5921
## 6 37.2068 nan 0.0500 2.4737
## 7 34.2648 nan 0.0500 2.6141
## 8 31.5883 nan 0.0500 2.5376
## 9 29.3471 nan 0.0500 2.1549
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## 40 5.9939 nan 0.0500 0.1748
## 60 4.0245 nan 0.0500 0.0138
## 80 3.4240 nan 0.0500 -0.0055
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## 180 2.5606 nan 0.0500 -0.0099
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## 280 2.0837 nan 0.0500 -0.0073
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1751 nan 0.0500 4.7242
## 2 51.9385 nan 0.0500 4.5791
## 3 47.7678 nan 0.0500 4.2664
## 4 43.9528 nan 0.0500 4.2168
## 5 40.4708 nan 0.0500 3.6818
## 6 37.4278 nan 0.0500 2.9515
## 7 34.7079 nan 0.0500 2.3996
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## 9 30.0835 nan 0.0500 1.9938
## 10 27.9190 nan 0.0500 2.0398
## 20 14.5739 nan 0.0500 0.8620
## 40 6.4316 nan 0.0500 0.1544
## 60 4.4438 nan 0.0500 0.0307
## 80 3.8964 nan 0.0500 -0.0230
## 100 3.5835 nan 0.0500 -0.0383
## 120 3.3612 nan 0.0500 -0.0109
## 140 3.1757 nan 0.0500 -0.0074
## 160 2.9966 nan 0.0500 -0.0189
## 180 2.8774 nan 0.0500 -0.0184
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## 220 2.6249 nan 0.0500 -0.0238
## 240 2.5440 nan 0.0500 -0.0091
## 260 2.4550 nan 0.0500 -0.0088
## 280 2.3690 nan 0.0500 -0.0134
## 300 2.2901 nan 0.0500 -0.0115
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.3137 nan 0.0500 5.1372
## 2 51.5330 nan 0.0500 4.5945
## 3 47.2745 nan 0.0500 3.9988
## 4 43.3392 nan 0.0500 4.2006
## 5 39.7695 nan 0.0500 3.4859
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## 20 12.6775 nan 0.0500 0.8647
## 40 4.6457 nan 0.0500 0.1575
## 60 3.0133 nan 0.0500 -0.0072
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## 180 1.4478 nan 0.0500 -0.0035
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## 220 1.2101 nan 0.0500 -0.0173
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.0808 nan 0.0500 5.5010
## 2 51.2346 nan 0.0500 4.4403
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## 40 5.0199 nan 0.0500 0.1273
## 60 3.3143 nan 0.0500 -0.0143
## 80 2.7825 nan 0.0500 -0.0033
## 100 2.4598 nan 0.0500 -0.0247
## 120 2.2223 nan 0.0500 -0.0133
## 140 2.0390 nan 0.0500 -0.0197
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## 180 1.7536 nan 0.0500 -0.0106
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## 220 1.5493 nan 0.0500 -0.0103
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## 280 1.3022 nan 0.0500 -0.0065
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 56.1240 nan 0.0500 4.9851
## 2 51.5271 nan 0.0500 4.7105
## 3 47.2978 nan 0.0500 3.9636
## 4 43.2374 nan 0.0500 3.9442
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## 40 5.2301 nan 0.0500 0.0377
## 60 3.8169 nan 0.0500 0.0036
## 80 3.3373 nan 0.0500 -0.0175
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## 180 2.3145 nan 0.0500 -0.0014
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## 260 1.8827 nan 0.0500 -0.0068
## 280 1.8105 nan 0.0500 -0.0106
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 53.6591 nan 0.1000 6.7715
## 2 48.3010 nan 0.1000 5.7867
## 3 42.4891 nan 0.1000 5.9609
## 4 38.2250 nan 0.1000 3.8666
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## 60 4.2664 nan 0.1000 -0.0266
## 80 3.8975 nan 0.1000 -0.0090
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## 180 3.3889 nan 0.1000 0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.0675 nan 0.1000 7.7076
## 2 47.5763 nan 0.1000 5.7988
## 3 42.2040 nan 0.1000 5.5087
## 4 37.9913 nan 0.1000 4.1518
## 5 34.5586 nan 0.1000 3.4527
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## 140 3.6115 nan 0.1000 -0.0079
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 54.0901 nan 0.1000 7.5111
## 2 47.7363 nan 0.1000 5.7829
## 3 42.5440 nan 0.1000 4.8537
## 4 38.7286 nan 0.1000 3.9717
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## 10 22.0039 nan 0.1000 1.4336
## 20 11.7075 nan 0.1000 0.3756
## 40 6.0787 nan 0.1000 0.0725
## 60 4.7798 nan 0.1000 -0.0153
## 80 4.4986 nan 0.1000 -0.0529
## 100 4.2960 nan 0.1000 -0.0206
## 120 4.1212 nan 0.1000 0.0028
## 140 3.9919 nan 0.1000 -0.0649
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## 180 3.7531 nan 0.1000 -0.0065
## 200 3.6914 nan 0.1000 -0.0238
## 220 3.6297 nan 0.1000 -0.0080
## 240 3.5573 nan 0.1000 -0.0091
## 260 3.5090 nan 0.1000 -0.0045
## 280 3.4254 nan 0.1000 -0.0096
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.6544 nan 0.1000 10.0984
## 2 43.7615 nan 0.1000 6.5339
## 3 36.8242 nan 0.1000 6.1128
## 4 31.6833 nan 0.1000 5.6614
## 5 27.0822 nan 0.1000 4.5967
## 6 23.7313 nan 0.1000 3.4297
## 7 20.8276 nan 0.1000 2.7725
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## 20 5.8131 nan 0.1000 0.2346
## 40 3.3491 nan 0.1000 -0.0282
## 60 2.7920 nan 0.1000 -0.0128
## 80 2.4312 nan 0.1000 -0.0156
## 100 2.1622 nan 0.1000 -0.0418
## 120 1.9378 nan 0.1000 -0.0372
## 140 1.7871 nan 0.1000 -0.0262
## 160 1.6445 nan 0.1000 -0.0153
## 180 1.5066 nan 0.1000 -0.0165
## 200 1.3807 nan 0.1000 -0.0146
## 220 1.2761 nan 0.1000 -0.0124
## 240 1.1967 nan 0.1000 -0.0102
## 260 1.1261 nan 0.1000 -0.0059
## 280 1.0537 nan 0.1000 -0.0243
## 300 0.9882 nan 0.1000 -0.0068
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.4786 nan 0.1000 9.7103
## 2 43.5594 nan 0.1000 8.0911
## 3 36.9666 nan 0.1000 6.6066
## 4 31.9142 nan 0.1000 6.0019
## 5 27.5164 nan 0.1000 4.0046
## 6 23.7718 nan 0.1000 3.4984
## 7 20.7906 nan 0.1000 2.8973
## 8 18.1682 nan 0.1000 2.4754
## 9 16.2047 nan 0.1000 1.8840
## 10 14.1921 nan 0.1000 1.8087
## 20 6.1461 nan 0.1000 0.3012
## 40 3.4691 nan 0.1000 -0.0183
## 60 2.9709 nan 0.1000 -0.0594
## 80 2.6037 nan 0.1000 -0.0039
## 100 2.3855 nan 0.1000 -0.0399
## 120 2.1780 nan 0.1000 -0.0203
## 140 1.9992 nan 0.1000 -0.0121
## 160 1.8866 nan 0.1000 -0.0140
## 180 1.7876 nan 0.1000 -0.0352
## 200 1.6674 nan 0.1000 -0.0230
## 220 1.5755 nan 0.1000 -0.0183
## 240 1.4904 nan 0.1000 -0.0258
## 260 1.4021 nan 0.1000 -0.0199
## 280 1.3356 nan 0.1000 -0.0088
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.7884 nan 0.1000 9.6041
## 2 43.5082 nan 0.1000 7.7349
## 3 36.7723 nan 0.1000 6.6486
## 4 31.2501 nan 0.1000 5.0619
## 5 27.4044 nan 0.1000 4.4218
## 6 23.8806 nan 0.1000 3.6108
## 7 20.8837 nan 0.1000 2.8841
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## 9 16.4330 nan 0.1000 1.5314
## 10 14.6498 nan 0.1000 1.7993
## 20 6.1358 nan 0.1000 0.2728
## 40 3.7223 nan 0.1000 -0.0273
## 60 3.2456 nan 0.1000 -0.0638
## 80 2.9398 nan 0.1000 -0.0105
## 100 2.7081 nan 0.1000 -0.0211
## 120 2.5264 nan 0.1000 -0.0281
## 140 2.3295 nan 0.1000 -0.0573
## 160 2.1631 nan 0.1000 -0.0171
## 180 2.0247 nan 0.1000 -0.0099
## 200 1.9037 nan 0.1000 -0.0195
## 220 1.8217 nan 0.1000 -0.0149
## 240 1.7207 nan 0.1000 -0.0115
## 260 1.6548 nan 0.1000 -0.0428
## 280 1.5657 nan 0.1000 -0.0149
## 300 1.4950 nan 0.1000 -0.0078
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 50.9588 nan 0.1000 9.4794
## 2 42.5804 nan 0.1000 8.4162
## 3 35.7943 nan 0.1000 6.1026
## 4 30.1986 nan 0.1000 5.0705
## 5 25.4983 nan 0.1000 4.6203
## 6 21.8833 nan 0.1000 3.3288
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## 9 14.1126 nan 0.1000 1.9322
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## 20 4.7097 nan 0.1000 0.2904
## 40 2.4959 nan 0.1000 -0.0006
## 60 1.9685 nan 0.1000 -0.0520
## 80 1.6370 nan 0.1000 -0.0200
## 100 1.3850 nan 0.1000 -0.0268
## 120 1.1901 nan 0.1000 -0.0074
## 140 1.0546 nan 0.1000 -0.0182
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## 180 0.7932 nan 0.1000 -0.0106
## 200 0.7076 nan 0.1000 -0.0139
## 220 0.6218 nan 0.1000 -0.0172
## 240 0.5482 nan 0.1000 -0.0112
## 260 0.4917 nan 0.1000 -0.0086
## 280 0.4423 nan 0.1000 -0.0106
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.0314 nan 0.1000 10.6135
## 2 42.7274 nan 0.1000 7.7280
## 3 35.9765 nan 0.1000 7.4152
## 4 30.3635 nan 0.1000 5.1352
## 5 25.7286 nan 0.1000 4.1265
## 6 21.8343 nan 0.1000 4.0708
## 7 18.6432 nan 0.1000 3.0269
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## 60 2.2100 nan 0.1000 -0.0393
## 80 1.9041 nan 0.1000 -0.0152
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## 120 1.4970 nan 0.1000 -0.0218
## 140 1.3284 nan 0.1000 -0.0142
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## 180 1.0333 nan 0.1000 -0.0112
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## 220 0.8640 nan 0.1000 -0.0118
## 240 0.7870 nan 0.1000 -0.0175
## 260 0.7268 nan 0.1000 -0.0274
## 280 0.6661 nan 0.1000 -0.0077
## 300 0.6106 nan 0.1000 -0.0092
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 51.3636 nan 0.1000 10.1561
## 2 42.7844 nan 0.1000 7.0321
## 3 35.9204 nan 0.1000 6.8687
## 4 30.4760 nan 0.1000 5.3386
## 5 25.7371 nan 0.1000 4.2528
## 6 22.0112 nan 0.1000 3.7777
## 7 18.9485 nan 0.1000 3.0504
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## 20 5.2737 nan 0.1000 0.1733
## 40 3.3011 nan 0.1000 -0.0077
## 60 2.8406 nan 0.1000 -0.0487
## 80 2.4461 nan 0.1000 -0.0250
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## 120 1.9425 nan 0.1000 -0.0073
## 140 1.7949 nan 0.1000 -0.0192
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## 180 1.5138 nan 0.1000 -0.0053
## 200 1.4078 nan 0.1000 -0.0071
## 220 1.2648 nan 0.1000 -0.0123
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## 260 1.0858 nan 0.1000 -0.0092
## 280 1.0228 nan 0.1000 -0.0249
## 300 0.9577 nan 0.1000 -0.0125
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 52.4616 nan 0.1000 10.3905
## 2 44.3583 nan 0.1000 8.1397
## 3 37.4620 nan 0.1000 6.1114
## 4 31.8007 nan 0.1000 5.6530
## 5 27.0376 nan 0.1000 4.5885
## 6 23.3893 nan 0.1000 3.3185
## 7 20.4557 nan 0.1000 2.7501
## 8 17.7660 nan 0.1000 2.5441
## 9 15.7836 nan 0.1000 2.0778
## 10 14.0228 nan 0.1000 1.7046
## 20 5.9281 nan 0.1000 0.2932
## 40 3.4548 nan 0.1000 -0.0049
## 60 2.9358 nan 0.1000 -0.0250
## 80 2.6003 nan 0.1000 -0.0225
## 100 2.3949 nan 0.1000 -0.0240
## 120 2.1777 nan 0.1000 -0.0255
## 140 2.0366 nan 0.1000 -0.0287
## 160 1.8911 nan 0.1000 -0.0440
## 180 1.7667 nan 0.1000 -0.0167
## 200 1.6584 nan 0.1000 -0.0249##################################
# Reporting the apparent results
# for the GBM model
##################################
GBM_DALEX <- DALEX::explain(GBM_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "GBM")
(GBM_DALEX_Performance <- model_performance(GBM_DALEX))## Measures for: regression
## mse : 1.658418
## rmse : 1.287796
## r2 : 0.9732008
## mad : 0.7400697
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -4.73887723 -1.34111721 -0.83535168 -0.49339743 -0.22544393 -0.05181421
## 60% 70% 80% 90% 100%
## 0.16238009 0.52825030 0.91403922 1.67095243 4.33309954(GBM_DALEX_Diagnostics <- model_diagnostics(GBM_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :55.18
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.96
## Median : 82.80 Median :4.717 Median :73.53 Median :73.23
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.45
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.71
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :84.94
## residuals abs_residuals label ids
## Min. :-4.73888 Min. :0.00415 Length:292 Min. : 1.00
## 1st Qu.:-0.70603 1st Qu.:0.27696 Class :character 1st Qu.: 73.75
## Median :-0.05181 Median :0.74007 Mode :character Median :146.50
## Mean : 0.02483 Mean :0.94179 Mean :146.50
## 3rd Qu.: 0.75568 3rd Qu.:1.29372 3rd Qu.:219.25
## Max. : 4.33310 Max. :4.73888 Max. :292.00plot(GBM_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("GBM: Observed and Predicted LIFEXP")
GBM_DALEX_VariableImportance <- model_parts(GBM_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(GBM_DALEX_VariableImportance)
##################################
# Reporting the cross-validation results
# for the GBM model
##################################
GBM_Tune## Stochastic Gradient Boosting
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## shrinkage interaction.depth n.minobsinnode n.trees RMSE Rsquared
## 0.01 1 5 100 4.772676 0.8333691
## 0.01 1 5 200 3.515429 0.8726404
## 0.01 1 5 300 2.877624 0.8964547
## 0.01 1 10 100 4.793621 0.8332069
## 0.01 1 10 200 3.521911 0.8733517
## 0.01 1 10 300 2.890117 0.8956765
## 0.01 1 15 100 4.793589 0.8339653
## 0.01 1 15 200 3.530219 0.8713678
## 0.01 1 15 300 2.904276 0.8956071
## 0.01 3 5 100 3.892040 0.8947209
## 0.01 3 5 200 2.654034 0.9139912
## 0.01 3 5 300 2.264173 0.9235081
## 0.01 3 10 100 3.896555 0.8970888
## 0.01 3 10 200 2.650019 0.9149851
## 0.01 3 10 300 2.270424 0.9234616
## 0.01 3 15 100 3.936460 0.8916345
## 0.01 3 15 200 2.697619 0.9125729
## 0.01 3 15 300 2.311237 0.9215290
## 0.01 5 5 100 3.698565 0.9106552
## 0.01 5 5 200 2.476083 0.9228052
## 0.01 5 5 300 2.189965 0.9274235
## 0.01 5 10 100 3.694360 0.9124533
## 0.01 5 10 200 2.483889 0.9223152
## 0.01 5 10 300 2.188089 0.9272777
## 0.01 5 15 100 3.743395 0.9073428
## 0.01 5 15 200 2.537516 0.9193448
## 0.01 5 15 300 2.252018 0.9238906
## 0.05 1 5 100 2.348301 0.9165106
## 0.05 1 5 200 2.164617 0.9255460
## 0.05 1 5 300 2.145105 0.9272272
## 0.05 1 10 100 2.371539 0.9151106
## 0.05 1 10 200 2.192438 0.9241736
## 0.05 1 10 300 2.175619 0.9254071
## 0.05 1 15 100 2.418639 0.9118379
## 0.05 1 15 200 2.246551 0.9215537
## 0.05 1 15 300 2.210928 0.9234890
## 0.05 3 5 100 2.132971 0.9279468
## 0.05 3 5 200 2.128585 0.9281739
## 0.05 3 5 300 2.138451 0.9271950
## 0.05 3 10 100 2.151650 0.9270914
## 0.05 3 10 200 2.134122 0.9280313
## 0.05 3 10 300 2.140481 0.9273580
## 0.05 3 15 100 2.210260 0.9236464
## 0.05 3 15 200 2.175687 0.9253416
## 0.05 3 15 300 2.171212 0.9250317
## 0.05 5 5 100 2.183111 0.9251933
## 0.05 5 5 200 2.197547 0.9232986
## 0.05 5 5 300 2.192592 0.9235604
## 0.05 5 10 100 2.125501 0.9289397
## 0.05 5 10 200 2.148236 0.9267642
## 0.05 5 10 300 2.149715 0.9262640
## 0.05 5 15 100 2.160418 0.9266001
## 0.05 5 15 200 2.173699 0.9253182
## 0.05 5 15 300 2.169584 0.9248442
## 0.10 1 5 100 2.216683 0.9224492
## 0.10 1 5 200 2.171832 0.9249045
## 0.10 1 5 300 2.163527 0.9254722
## 0.10 1 10 100 2.190542 0.9240748
## 0.10 1 10 200 2.135899 0.9276319
## 0.10 1 10 300 2.132410 0.9273953
## 0.10 1 15 100 2.303119 0.9177669
## 0.10 1 15 200 2.211956 0.9236257
## 0.10 1 15 300 2.191344 0.9244899
## 0.10 3 5 100 2.165442 0.9258963
## 0.10 3 5 200 2.157851 0.9261405
## 0.10 3 5 300 2.165798 0.9247466
## 0.10 3 10 100 2.131881 0.9280289
## 0.10 3 10 200 2.107162 0.9285203
## 0.10 3 10 300 2.135974 0.9258930
## 0.10 3 15 100 2.169556 0.9263259
## 0.10 3 15 200 2.161955 0.9250116
## 0.10 3 15 300 2.145423 0.9254143
## 0.10 5 5 100 2.193752 0.9240341
## 0.10 5 5 200 2.214680 0.9221060
## 0.10 5 5 300 2.218715 0.9214655
## 0.10 5 10 100 2.212554 0.9219448
## 0.10 5 10 200 2.239859 0.9195548
## 0.10 5 10 300 2.261049 0.9178753
## 0.10 5 15 100 2.189393 0.9242153
## 0.10 5 15 200 2.205440 0.9225160
## 0.10 5 15 300 2.194315 0.9223180
## MAE
## 3.858956
## 2.840899
## 2.288121
## 3.875879
## 2.845201
## 2.289532
## 3.869335
## 2.827283
## 2.279083
## 3.222475
## 2.104761
## 1.721413
## 3.232783
## 2.091787
## 1.710329
## 3.246277
## 2.109478
## 1.728179
## 3.068383
## 1.947763
## 1.654183
## 3.060625
## 1.945668
## 1.626496
## 3.087076
## 1.962356
## 1.666117
## 1.798163
## 1.599649
## 1.579040
## 1.802411
## 1.633652
## 1.615435
## 1.815861
## 1.661022
## 1.629704
## 1.580361
## 1.572619
## 1.582000
## 1.592463
## 1.563340
## 1.574442
## 1.624147
## 1.596711
## 1.587831
## 1.609724
## 1.604475
## 1.612752
## 1.566812
## 1.581413
## 1.586848
## 1.574829
## 1.585161
## 1.574231
## 1.644726
## 1.591006
## 1.584273
## 1.610851
## 1.562029
## 1.575392
## 1.708563
## 1.627455
## 1.610661
## 1.593192
## 1.608458
## 1.631719
## 1.570714
## 1.559041
## 1.593043
## 1.575222
## 1.593423
## 1.573554
## 1.617333
## 1.630894
## 1.638725
## 1.617015
## 1.634059
## 1.649568
## 1.616787
## 1.619968
## 1.617048
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were n.trees = 200, interaction.depth =
## 3, shrinkage = 0.1 and n.minobsinnode = 10.GBM_Tune$finalModel## A gradient boosted model with gaussian loss function.
## 200 iterations were performed.
## There were 6 predictors of which 6 had non-zero influence.(GBM_Tune_RMSE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("RMSE")])## [1] 2.107162(GBM_Tune_Rsquared <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("Rsquared")])## [1] 0.9285203(GBM_Tune_MAE <- GBM_Tune$results[GBM_Tune$results$shrinkage==GBM_Tune$bestTune$shrinkage &
GBM_Tune$results$interaction.depth==GBM_Tune$bestTune$interaction.depth &
GBM_Tune$results$n.minobsinnode==GBM_Tune$bestTune$n.minobsinnode &
GBM_Tune$results$n.trees==GBM_Tune$bestTune$n.trees,
c("MAE")])## [1] 1.5590411.3.6.2 Random Forest (RF)
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the RF model
##################################
RF_Grid = data.frame(mtry = c(100, 200, 300, 400, 500,
600, 700, 800, 900, 1000))
##################################
# Running the RF model
# by setting the caret method to 'RF'
##################################
set.seed(12345678)
RF_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "rf",
tuneGrid = RF_Grid,
trControl = KFold_Control)
##################################
# Reporting the apparent results
# for the RF model
##################################
RF_DALEX <- DALEX::explain(RF_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "RF")
(RF_DALEX_Performance <- model_performance(RF_DALEX))## Measures for: regression
## mse : 0.9404626
## rmse : 0.9697745
## r2 : 0.9848026
## mad : 0.493929
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -3.89311943 -1.04739946 -0.63797813 -0.37774333 -0.17489179 -0.02355452
## 60% 70% 80% 90% 100%
## 0.11686047 0.37368288 0.59428965 1.20572149 3.83678360(RF_DALEX_Diagnostics <- model_diagnostics(RF_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :54.37
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:67.15
## Median : 82.80 Median :4.717 Median :73.53 Median :73.58
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.49
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.49
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :86.25
## residuals abs_residuals label ids
## Min. :-3.89312 Min. :0.004567 Length:292 Min. : 1.00
## 1st Qu.:-0.49373 1st Qu.:0.228644 Class :character 1st Qu.: 73.75
## Median :-0.02355 Median :0.493929 Mode :character Median :146.50
## Mean :-0.01104 Mean :0.695743 Mean :146.50
## 3rd Qu.: 0.49410 3rd Qu.:0.996858 3rd Qu.:219.25
## Max. : 3.83678 Max. :3.893119 Max. :292.00plot(RF_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")
RF_DALEX_VariableImportance <- model_parts(RF_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(RF_DALEX_VariableImportance)
##################################
# Reporting the cross-validation results
# for the RF model
##################################
RF_Tune## Random Forest
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## mtry RMSE Rsquared MAE
## 100 2.244547 0.9196737 1.619943
## 200 2.249565 0.9196008 1.620459
## 300 2.251214 0.9194065 1.628754
## 400 2.239011 0.9201778 1.618476
## 500 2.244594 0.9199741 1.623557
## 600 2.250218 0.9195438 1.627748
## 700 2.250948 0.9195926 1.627966
## 800 2.252804 0.9194439 1.626298
## 900 2.256902 0.9191818 1.630857
## 1000 2.245525 0.9198362 1.618343
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 400.RF_Tune$finalModel##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 6
##
## Mean of squared residuals: 5.126832
## % Var explained: 91.72(RF_Tune_RMSE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("RMSE")])## [1] 2.239011(RF_Tune_Rsquared <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("Rsquared")])## [1] 0.9201778(RF_Tune_MAE <- RF_Tune$results[RF_Tune$results$mtry==RF_Tune$bestTune$mtry,
c("MAE")])## [1] 1.6184761.3.6.3 Neural Network (NN)
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the NN model
##################################
NN_Grid = expand.grid(size = c(2, 5, 10, 15, 20),
decay = c(0, 0.1, 0.001, 0.0001, 0.00001))
##################################
# Running the NN model
# by setting the caret method to 'NN'
##################################
set.seed(12345678)
NN_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "nnet",
linout = TRUE,
preProcess = c('center', 'scale'),
maxit = 500,
tuneGrid = NN_Grid,
trControl = KFold_Control)## # weights: 25
## initial value 1400339.778929
## iter 10 value 68496.910319
## iter 20 value 5956.174709
## iter 30 value 5844.262957
## iter 40 value 5352.739998
## iter 50 value 5259.580299
## iter 60 value 5258.019066
## iter 70 value 5205.090145
## iter 80 value 5196.195489
## iter 90 value 4833.831031
## iter 100 value 4569.510587
## iter 110 value 4553.721628
## iter 120 value 4553.045122
## iter 130 value 4553.015144
## iter 140 value 4552.876106
## iter 150 value 4550.489828
## iter 160 value 4550.208091
## iter 170 value 4550.152984
## iter 180 value 4550.031282
## iter 190 value 4549.764089
## iter 200 value 4549.444274
## iter 210 value 4548.348059
## iter 220 value 4548.157434
## iter 220 value 4548.157430
## iter 220 value 4548.157430
## final value 4548.157430
## converged
## # weights: 61
## initial value 1419402.900363
## iter 10 value 5466.160778
## iter 20 value 4136.215442
## iter 30 value 3233.377310
## iter 40 value 2184.748970
## iter 50 value 1482.694097
## iter 60 value 1341.742361
## iter 70 value 1297.232211
## iter 80 value 1213.738131
## iter 90 value 1146.376381
## iter 100 value 1070.241735
## iter 110 value 1030.232654
## iter 120 value 1015.217766
## iter 130 value 1005.639574
## iter 140 value 998.773821
## iter 150 value 992.324116
## iter 160 value 980.531489
## iter 170 value 978.495782
## iter 180 value 978.409744
## iter 190 value 978.361243
## iter 200 value 978.318025
## iter 210 value 978.296926
## iter 220 value 968.807011
## iter 230 value 939.853517
## iter 240 value 925.037978
## iter 250 value 919.646061
## iter 260 value 919.163802
## iter 270 value 917.547708
## iter 280 value 914.888136
## iter 290 value 911.397634
## iter 300 value 909.574590
## iter 310 value 908.742002
## iter 320 value 908.217371
## iter 330 value 907.307508
## iter 340 value 906.728904
## iter 350 value 906.413133
## iter 360 value 906.376723
## iter 370 value 906.329715
## iter 380 value 906.279582
## iter 390 value 906.271020
## iter 400 value 906.268970
## final value 906.268949
## converged
## # weights: 121
## initial value 1369973.913442
## iter 10 value 1456.280282
## iter 20 value 981.019262
## iter 30 value 820.891644
## iter 40 value 691.064010
## iter 50 value 615.847093
## iter 60 value 552.618476
## iter 70 value 501.933326
## iter 80 value 465.287566
## iter 90 value 435.639678
## iter 100 value 411.632837
## iter 110 value 377.319124
## iter 120 value 355.295277
## iter 130 value 332.770009
## iter 140 value 319.998174
## iter 150 value 311.670112
## iter 160 value 296.995967
## iter 170 value 289.673758
## iter 180 value 286.817395
## iter 190 value 285.327350
## iter 200 value 283.822117
## iter 210 value 281.823769
## iter 220 value 280.604183
## iter 230 value 279.960985
## iter 240 value 279.298747
## iter 250 value 278.767676
## iter 260 value 278.387227
## iter 270 value 277.570900
## iter 280 value 276.221841
## iter 290 value 273.862125
## iter 300 value 271.110331
## iter 310 value 269.686949
## iter 320 value 268.329167
## iter 330 value 266.353429
## iter 340 value 263.244321
## iter 350 value 262.025670
## iter 360 value 260.468974
## iter 370 value 259.082188
## iter 380 value 258.024935
## iter 390 value 257.709959
## iter 400 value 257.192116
## iter 410 value 256.946798
## iter 420 value 256.805007
## iter 430 value 256.770626
## iter 440 value 256.766595
## iter 450 value 256.763944
## iter 460 value 256.762805
## iter 470 value 256.759951
## iter 480 value 256.742706
## final value 256.731685
## converged
## # weights: 181
## initial value 1369859.779729
## iter 10 value 1218.791825
## iter 20 value 844.152299
## iter 30 value 679.366662
## iter 40 value 572.273305
## iter 50 value 466.657882
## iter 60 value 383.138573
## iter 70 value 339.951431
## iter 80 value 283.173217
## iter 90 value 257.527205
## iter 100 value 237.333601
## iter 110 value 223.739667
## iter 120 value 215.169024
## iter 130 value 208.082918
## iter 140 value 199.329810
## iter 150 value 191.342838
## iter 160 value 184.771082
## iter 170 value 174.014610
## iter 180 value 167.789067
## iter 190 value 161.398029
## iter 200 value 156.888476
## iter 210 value 153.435980
## iter 220 value 149.579761
## iter 230 value 146.046728
## iter 240 value 142.808104
## iter 250 value 140.214188
## iter 260 value 137.517961
## iter 270 value 135.175708
## iter 280 value 132.482019
## iter 290 value 129.935560
## iter 300 value 127.301305
## iter 310 value 124.632966
## iter 320 value 122.671009
## iter 330 value 119.063976
## iter 340 value 115.950398
## iter 350 value 113.321558
## iter 360 value 110.553146
## iter 370 value 109.537855
## iter 380 value 108.980326
## iter 390 value 108.142979
## iter 400 value 107.033356
## iter 410 value 106.482910
## iter 420 value 105.642504
## iter 430 value 104.684540
## iter 440 value 103.914639
## iter 450 value 102.861627
## iter 460 value 101.598500
## iter 470 value 99.744133
## iter 480 value 97.351172
## iter 490 value 95.563516
## iter 500 value 93.616614
## final value 93.616614
## stopped after 500 iterations
## # weights: 241
## initial value 1362136.072048
## iter 10 value 1398.158403
## iter 20 value 813.986040
## iter 30 value 623.314450
## iter 40 value 451.572085
## iter 50 value 343.423349
## iter 60 value 299.481028
## iter 70 value 258.539324
## iter 80 value 218.722855
## iter 90 value 189.014309
## iter 100 value 172.421623
## iter 110 value 152.085135
## iter 120 value 140.177393
## iter 130 value 130.443034
## iter 140 value 121.601687
## iter 150 value 114.523579
## iter 160 value 107.718749
## iter 170 value 97.564388
## iter 180 value 92.637160
## iter 190 value 87.632255
## iter 200 value 83.334630
## iter 210 value 78.936737
## iter 220 value 74.404872
## iter 230 value 71.245308
## iter 240 value 69.350806
## iter 250 value 66.556843
## iter 260 value 63.476653
## iter 270 value 61.090865
## iter 280 value 59.602674
## iter 290 value 58.022193
## iter 300 value 55.931747
## iter 310 value 54.279973
## iter 320 value 52.788004
## iter 330 value 52.144060
## iter 340 value 51.520501
## iter 350 value 50.937822
## iter 360 value 50.600355
## iter 370 value 50.390622
## iter 380 value 50.202609
## iter 390 value 49.915583
## iter 400 value 49.675294
## iter 410 value 49.500596
## iter 420 value 49.385656
## iter 430 value 49.276835
## iter 440 value 49.181953
## iter 450 value 49.072194
## iter 460 value 48.923516
## iter 470 value 48.704723
## iter 480 value 48.485576
## iter 490 value 48.352389
## iter 500 value 48.312069
## final value 48.312069
## stopped after 500 iterations
## # weights: 25
## initial value 1384018.650104
## iter 10 value 15568.108680
## iter 20 value 7078.471518
## iter 30 value 5823.550517
## iter 40 value 4681.910341
## iter 50 value 3892.420056
## iter 60 value 2611.064130
## iter 70 value 2192.772551
## iter 80 value 2005.636016
## iter 90 value 1807.236078
## iter 100 value 1636.352081
## iter 110 value 1526.444834
## iter 120 value 1483.669235
## iter 130 value 1476.459207
## iter 140 value 1448.987229
## iter 150 value 1442.620774
## final value 1442.569494
## converged
## # weights: 61
## initial value 1394098.585171
## iter 10 value 26536.454537
## iter 20 value 11933.656186
## iter 30 value 8409.704475
## iter 40 value 7714.766464
## iter 50 value 6574.341375
## iter 60 value 4426.666175
## iter 70 value 3181.854615
## iter 80 value 2165.503641
## iter 90 value 1589.793031
## iter 100 value 1395.847733
## iter 110 value 1303.531922
## iter 120 value 1202.196454
## iter 130 value 1137.112382
## iter 140 value 1094.154891
## iter 150 value 1051.603814
## iter 160 value 1040.897230
## iter 170 value 1024.371791
## iter 180 value 998.908996
## iter 190 value 980.361705
## iter 200 value 968.920478
## iter 210 value 960.269924
## iter 220 value 958.949560
## iter 230 value 955.625569
## iter 240 value 954.622306
## iter 250 value 954.575117
## iter 260 value 954.545205
## final value 954.529372
## converged
## # weights: 121
## initial value 1387428.049031
## iter 10 value 1450.061966
## iter 20 value 965.028030
## iter 30 value 832.187617
## iter 40 value 742.640384
## iter 50 value 706.951827
## iter 60 value 671.165099
## iter 70 value 655.198531
## iter 80 value 640.955382
## iter 90 value 631.411994
## iter 100 value 623.066768
## iter 110 value 616.700789
## iter 120 value 608.599648
## iter 130 value 600.341217
## iter 140 value 592.731460
## iter 150 value 588.543437
## iter 160 value 584.942937
## iter 170 value 579.711382
## iter 180 value 574.896506
## iter 190 value 570.791378
## iter 200 value 565.916376
## iter 210 value 561.811951
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## iter 230 value 555.115952
## iter 240 value 552.601403
## iter 250 value 551.234121
## iter 260 value 549.812405
## iter 270 value 545.883743
## iter 280 value 543.309314
## iter 290 value 540.965899
## iter 300 value 539.338412
## iter 310 value 537.312268
## iter 320 value 534.414925
## iter 330 value 532.313464
## iter 340 value 530.388935
## iter 350 value 528.555728
## iter 360 value 527.856224
## iter 370 value 527.200364
## iter 380 value 526.704580
## iter 390 value 526.585428
## iter 400 value 526.560876
## iter 410 value 526.557217
## iter 420 value 526.556435
## iter 430 value 526.556285
## final value 526.556224
## converged
## # weights: 181
## initial value 1353091.485763
## iter 10 value 1287.823903
## iter 20 value 938.702167
## iter 30 value 748.096569
## iter 40 value 639.041680
## iter 50 value 556.282172
## iter 60 value 513.004807
## iter 70 value 483.894119
## iter 80 value 465.535296
## iter 90 value 449.084974
## iter 100 value 433.926647
## iter 110 value 425.401047
## iter 120 value 418.452940
## iter 130 value 414.162494
## iter 140 value 408.433218
## iter 150 value 404.732510
## iter 160 value 400.845451
## iter 170 value 397.982738
## iter 180 value 396.561282
## iter 190 value 395.014302
## iter 200 value 393.463659
## iter 210 value 391.791284
## iter 220 value 388.330655
## iter 230 value 386.385810
## iter 240 value 385.284808
## iter 250 value 383.783272
## iter 260 value 382.369491
## iter 270 value 381.739305
## iter 280 value 381.211069
## iter 290 value 379.185604
## iter 300 value 376.938740
## iter 310 value 376.298644
## iter 320 value 375.985631
## iter 330 value 375.767452
## iter 340 value 375.640929
## iter 350 value 375.399286
## iter 360 value 374.985965
## iter 370 value 374.872178
## iter 380 value 374.823068
## iter 390 value 374.719717
## iter 400 value 374.630241
## iter 410 value 374.580365
## iter 420 value 374.540869
## iter 430 value 374.492645
## iter 440 value 374.432551
## iter 450 value 374.330813
## iter 460 value 374.220902
## iter 470 value 374.157806
## iter 480 value 374.022897
## iter 490 value 373.172452
## iter 500 value 372.719725
## final value 372.719725
## stopped after 500 iterations
## # weights: 241
## initial value 1379427.806099
## iter 10 value 1318.482661
## iter 20 value 948.464684
## iter 30 value 781.314962
## iter 40 value 694.439588
## iter 50 value 613.809462
## iter 60 value 546.586849
## iter 70 value 499.266011
## iter 80 value 473.301240
## iter 90 value 453.074641
## iter 100 value 434.018863
## iter 110 value 422.579540
## iter 120 value 415.664194
## iter 130 value 409.582708
## iter 140 value 404.703083
## iter 150 value 400.088954
## iter 160 value 396.840796
## iter 170 value 393.550983
## iter 180 value 389.392143
## iter 190 value 384.520420
## iter 200 value 380.059544
## iter 210 value 376.737395
## iter 220 value 373.795749
## iter 230 value 371.647825
## iter 240 value 369.437417
## iter 250 value 367.198938
## iter 260 value 365.469468
## iter 270 value 363.797594
## iter 280 value 361.902571
## iter 290 value 360.302742
## iter 300 value 358.834282
## iter 310 value 357.653361
## iter 320 value 356.220173
## iter 330 value 354.742659
## iter 340 value 353.187152
## iter 350 value 352.317298
## iter 360 value 351.597133
## iter 370 value 350.748670
## iter 380 value 350.035629
## iter 390 value 349.251348
## iter 400 value 348.627508
## iter 410 value 348.035506
## iter 420 value 347.554107
## iter 430 value 347.055057
## iter 440 value 346.506821
## iter 450 value 346.028052
## iter 460 value 345.701868
## iter 470 value 345.448568
## iter 480 value 345.273019
## iter 490 value 345.171795
## iter 500 value 345.004260
## final value 345.004260
## stopped after 500 iterations
## # weights: 25
## initial value 1423891.582406
## iter 10 value 12537.489344
## iter 20 value 8269.674014
## iter 30 value 5421.520107
## iter 40 value 1535.106046
## iter 50 value 1444.459711
## iter 60 value 1406.797260
## iter 70 value 1230.493759
## iter 80 value 1084.242081
## iter 90 value 960.172332
## iter 100 value 943.333703
## iter 110 value 942.305098
## iter 120 value 930.335846
## iter 130 value 929.577196
## iter 140 value 928.927542
## iter 150 value 928.888316
## iter 160 value 928.401816
## iter 170 value 928.384803
## iter 180 value 926.787741
## iter 190 value 925.593135
## iter 200 value 925.551762
## iter 210 value 925.462572
## iter 220 value 924.972394
## iter 230 value 924.793673
## iter 240 value 921.986206
## iter 250 value 921.375885
## iter 260 value 921.237857
## iter 270 value 921.223130
## iter 280 value 921.220621
## iter 290 value 921.154089
## iter 300 value 921.110078
## iter 310 value 921.097205
## final value 921.097031
## converged
## # weights: 61
## initial value 1412917.868612
## iter 10 value 47303.215826
## iter 20 value 11772.187478
## iter 30 value 9642.666220
## iter 40 value 7039.730221
## iter 50 value 3438.511113
## iter 60 value 2153.777110
## iter 70 value 1457.745486
## iter 80 value 1089.836911
## iter 90 value 980.416563
## iter 100 value 933.168147
## iter 110 value 889.400530
## iter 120 value 870.160480
## iter 130 value 864.401688
## iter 140 value 857.626325
## iter 150 value 850.453945
## iter 160 value 841.635431
## iter 170 value 821.291746
## iter 180 value 805.288555
## iter 190 value 800.439409
## iter 200 value 793.712157
## iter 210 value 782.194383
## iter 220 value 776.109391
## iter 230 value 765.630829
## iter 240 value 753.827264
## iter 250 value 750.095525
## iter 260 value 747.055166
## iter 270 value 744.176972
## iter 280 value 738.447826
## iter 290 value 730.003868
## iter 300 value 716.537518
## iter 310 value 715.365196
## iter 320 value 713.454410
## iter 330 value 711.280430
## iter 340 value 709.210457
## iter 350 value 707.570229
## iter 360 value 706.449745
## iter 370 value 705.894708
## iter 380 value 705.207222
## iter 390 value 705.024693
## iter 400 value 704.997233
## iter 410 value 704.995416
## iter 420 value 704.994876
## iter 420 value 704.994873
## iter 420 value 704.994873
## final value 704.994873
## converged
## # weights: 121
## initial value 1433584.753726
## iter 10 value 2356.920606
## iter 20 value 1146.044953
## iter 30 value 894.843159
## iter 40 value 748.909966
## iter 50 value 659.434098
## iter 60 value 583.222552
## iter 70 value 521.519816
## iter 80 value 490.932249
## iter 90 value 466.770159
## iter 100 value 449.518369
## iter 110 value 421.297782
## iter 120 value 396.615299
## iter 130 value 383.763143
## iter 140 value 376.869871
## iter 150 value 365.351570
## iter 160 value 356.752188
## iter 170 value 347.514557
## iter 180 value 339.875712
## iter 190 value 334.620596
## iter 200 value 327.643579
## iter 210 value 324.005633
## iter 220 value 321.439653
## iter 230 value 318.718763
## iter 240 value 316.720846
## iter 250 value 316.349474
## iter 260 value 316.258847
## iter 270 value 316.004443
## iter 280 value 315.429764
## iter 290 value 314.608690
## iter 300 value 314.017114
## iter 310 value 313.057631
## iter 320 value 311.650614
## iter 330 value 303.149669
## iter 340 value 292.307685
## iter 350 value 283.880966
## iter 360 value 278.707246
## iter 370 value 275.872878
## iter 380 value 274.003154
## iter 390 value 272.594295
## iter 400 value 271.087957
## iter 410 value 269.693184
## iter 420 value 268.877864
## iter 430 value 267.877948
## iter 440 value 267.495669
## iter 450 value 267.155985
## iter 460 value 266.930277
## iter 470 value 266.848237
## iter 480 value 266.813716
## iter 490 value 266.803428
## iter 500 value 266.802076
## final value 266.802076
## stopped after 500 iterations
## # weights: 181
## initial value 1394930.794764
## iter 10 value 1279.269553
## iter 20 value 868.323818
## iter 30 value 719.556231
## iter 40 value 561.315354
## iter 50 value 464.560336
## iter 60 value 412.004354
## iter 70 value 373.089421
## iter 80 value 327.225673
## iter 90 value 296.791422
## iter 100 value 275.365839
## iter 110 value 258.632874
## iter 120 value 247.519543
## iter 130 value 238.913534
## iter 140 value 224.320611
## iter 150 value 211.431281
## iter 160 value 195.649973
## iter 170 value 184.992278
## iter 180 value 179.439211
## iter 190 value 174.741821
## iter 200 value 171.307787
## iter 210 value 167.550830
## iter 220 value 164.440970
## iter 230 value 162.559312
## iter 240 value 159.905158
## iter 250 value 158.674720
## iter 260 value 157.792217
## iter 270 value 156.438216
## iter 280 value 154.814089
## iter 290 value 153.285061
## iter 300 value 152.266027
## iter 310 value 151.416318
## iter 320 value 150.592960
## iter 330 value 149.935923
## iter 340 value 149.200792
## iter 350 value 148.616745
## iter 360 value 148.272016
## iter 370 value 148.148050
## iter 380 value 148.073069
## iter 390 value 147.871002
## iter 400 value 147.648666
## iter 410 value 147.288195
## iter 420 value 146.711104
## iter 430 value 145.937980
## iter 440 value 145.451685
## iter 450 value 144.939674
## iter 460 value 144.271039
## iter 470 value 143.147613
## iter 480 value 141.523488
## iter 490 value 139.291811
## iter 500 value 136.917199
## final value 136.917199
## stopped after 500 iterations
## # weights: 241
## initial value 1415447.001248
## iter 10 value 1560.997067
## iter 20 value 900.702298
## iter 30 value 647.249066
## iter 40 value 492.717841
## iter 50 value 405.391326
## iter 60 value 345.438590
## iter 70 value 306.750507
## iter 80 value 267.845476
## iter 90 value 235.799128
## iter 100 value 203.677835
## iter 110 value 183.091395
## iter 120 value 166.683630
## iter 130 value 150.230650
## iter 140 value 138.325315
## iter 150 value 126.601246
## iter 160 value 117.089372
## iter 170 value 109.712789
## iter 180 value 104.980911
## iter 190 value 100.729998
## iter 200 value 97.313505
## iter 210 value 94.693012
## iter 220 value 91.855427
## iter 230 value 88.698395
## iter 240 value 86.415252
## iter 250 value 84.194762
## iter 260 value 81.902251
## iter 270 value 79.161843
## iter 280 value 76.602614
## iter 290 value 74.852013
## iter 300 value 72.364602
## iter 310 value 70.525242
## iter 320 value 69.077758
## iter 330 value 67.575295
## iter 340 value 66.004828
## iter 350 value 64.995929
## iter 360 value 64.086367
## iter 370 value 63.280264
## iter 380 value 62.223884
## iter 390 value 61.269493
## iter 400 value 60.774563
## iter 410 value 59.812007
## iter 420 value 58.774771
## iter 430 value 58.390361
## iter 440 value 58.186240
## iter 450 value 58.035690
## iter 460 value 57.846932
## iter 470 value 57.675788
## iter 480 value 57.473200
## iter 490 value 57.338265
## iter 500 value 57.308874
## final value 57.308874
## stopped after 500 iterations
## # weights: 25
## initial value 1386870.681414
## iter 10 value 16073.496682
## iter 20 value 15376.477898
## iter 30 value 11345.105417
## iter 40 value 10198.597925
## iter 50 value 6428.790073
## iter 60 value 5644.346354
## iter 70 value 5497.965330
## iter 80 value 5421.132636
## iter 90 value 5179.625122
## iter 100 value 4884.074564
## iter 110 value 4234.055230
## iter 120 value 2904.598017
## iter 130 value 1556.481872
## iter 140 value 1388.490514
## iter 150 value 1364.959883
## iter 160 value 1354.867166
## iter 170 value 1328.410308
## iter 180 value 1319.516961
## iter 190 value 1314.239222
## iter 200 value 1311.288587
## iter 210 value 1309.937188
## iter 220 value 1309.893532
## iter 230 value 1307.847786
## iter 240 value 1294.078490
## iter 250 value 1283.816175
## iter 260 value 1278.919102
## iter 270 value 1270.853058
## iter 280 value 1258.307996
## iter 290 value 1252.139480
## iter 300 value 1232.788080
## iter 310 value 1199.651938
## iter 320 value 1172.256003
## iter 330 value 1166.258096
## iter 340 value 1163.088009
## iter 350 value 1162.089573
## iter 360 value 1160.698645
## iter 370 value 1133.349758
## iter 380 value 1079.030491
## iter 390 value 1074.131190
## iter 400 value 1073.353709
## iter 410 value 1072.970806
## iter 420 value 1070.957063
## iter 430 value 1068.827441
## iter 440 value 1068.618943
## final value 1068.618146
## converged
## # weights: 61
## initial value 1369944.309207
## iter 10 value 4813.258129
## iter 20 value 2647.028707
## iter 30 value 2018.882759
## iter 40 value 1448.844785
## iter 50 value 1310.679541
## iter 60 value 1237.240533
## iter 70 value 1144.330366
## iter 80 value 930.396868
## iter 90 value 844.047470
## iter 100 value 813.793452
## iter 110 value 799.122112
## iter 120 value 791.694476
## iter 130 value 785.215152
## iter 140 value 780.425639
## iter 150 value 773.020497
## iter 160 value 768.206613
## iter 170 value 755.018782
## iter 180 value 753.053456
## iter 190 value 749.706268
## iter 200 value 748.552178
## iter 210 value 741.293601
## iter 220 value 722.928566
## iter 230 value 718.856657
## iter 240 value 711.482516
## iter 250 value 703.614285
## iter 260 value 688.171332
## iter 270 value 686.786797
## iter 280 value 686.583852
## iter 290 value 686.442883
## iter 300 value 686.361656
## iter 310 value 684.560410
## iter 320 value 676.478486
## iter 330 value 675.804383
## iter 340 value 675.380648
## iter 350 value 674.316048
## iter 360 value 673.271927
## iter 370 value 672.798969
## iter 380 value 672.329418
## iter 390 value 672.184275
## iter 400 value 672.069031
## iter 410 value 671.911016
## iter 420 value 671.861759
## iter 430 value 671.662889
## iter 440 value 671.563037
## iter 450 value 671.496963
## iter 460 value 671.492484
## iter 470 value 671.488292
## iter 480 value 671.485325
## iter 490 value 671.482358
## iter 490 value 671.482352
## iter 500 value 671.480801
## final value 671.480801
## stopped after 500 iterations
## # weights: 121
## initial value 1448634.114928
## iter 10 value 13322.704711
## iter 20 value 4075.345309
## iter 30 value 3080.568831
## iter 40 value 2774.136853
## iter 50 value 2362.916182
## iter 60 value 2080.977179
## iter 70 value 1883.607992
## iter 80 value 1479.718416
## iter 90 value 1357.647737
## iter 100 value 1166.263645
## iter 110 value 1038.656966
## iter 120 value 910.916558
## iter 130 value 871.834875
## iter 140 value 845.580955
## iter 150 value 836.637035
## iter 160 value 830.920086
## iter 170 value 822.754485
## iter 180 value 807.472361
## iter 190 value 798.551929
## iter 200 value 796.890960
## iter 210 value 789.559091
## iter 220 value 768.048484
## iter 230 value 760.768959
## iter 240 value 750.867893
## iter 250 value 747.409155
## iter 260 value 746.976263
## iter 270 value 737.868555
## iter 280 value 721.564300
## iter 290 value 717.783889
## iter 300 value 714.155859
## iter 310 value 714.000603
## iter 320 value 709.916810
## iter 330 value 702.465299
## iter 340 value 696.906456
## iter 350 value 690.620258
## iter 360 value 681.004312
## iter 370 value 672.140070
## iter 380 value 667.202449
## iter 390 value 657.951654
## iter 400 value 636.257278
## iter 410 value 629.163597
## iter 420 value 626.709340
## iter 430 value 624.262775
## iter 440 value 616.050756
## iter 450 value 604.893254
## iter 460 value 600.444007
## iter 470 value 600.302006
## iter 480 value 600.114035
## iter 490 value 595.541228
## iter 500 value 585.092013
## final value 585.092013
## stopped after 500 iterations
## # weights: 181
## initial value 1413917.762241
## iter 10 value 1208.414670
## iter 20 value 780.378653
## iter 30 value 659.898262
## iter 40 value 534.589345
## iter 50 value 438.795352
## iter 60 value 383.367617
## iter 70 value 353.562699
## iter 80 value 308.163367
## iter 90 value 285.129490
## iter 100 value 269.971590
## iter 110 value 256.359031
## iter 120 value 246.275217
## iter 130 value 240.603830
## iter 140 value 234.079948
## iter 150 value 222.710452
## iter 160 value 215.770746
## iter 170 value 208.990527
## iter 180 value 204.697435
## iter 190 value 200.427219
## iter 200 value 197.721322
## iter 210 value 194.155305
## iter 220 value 191.774604
## iter 230 value 189.688585
## iter 240 value 188.116083
## iter 250 value 186.456308
## iter 260 value 184.470036
## iter 270 value 182.670852
## iter 280 value 179.775918
## iter 290 value 176.698129
## iter 300 value 173.555253
## iter 310 value 171.106637
## iter 320 value 169.713848
## iter 330 value 168.716446
## iter 340 value 167.417298
## iter 350 value 166.109809
## iter 360 value 164.690294
## iter 370 value 163.663174
## iter 380 value 163.111452
## iter 390 value 162.145033
## iter 400 value 160.248573
## iter 410 value 159.111763
## iter 420 value 157.290832
## iter 430 value 155.851294
## iter 440 value 154.377538
## iter 450 value 152.744293
## iter 460 value 151.144088
## iter 470 value 150.015990
## iter 480 value 148.359773
## iter 490 value 145.605921
## iter 500 value 142.279220
## final value 142.279220
## stopped after 500 iterations
## # weights: 241
## initial value 1408462.442303
## iter 10 value 1696.574721
## iter 20 value 834.666584
## iter 30 value 643.461071
## iter 40 value 499.893272
## iter 50 value 396.200813
## iter 60 value 352.163182
## iter 70 value 294.997125
## iter 80 value 262.226205
## iter 90 value 232.559809
## iter 100 value 213.077272
## iter 110 value 192.442938
## iter 120 value 175.768022
## iter 130 value 161.839371
## iter 140 value 152.054105
## iter 150 value 144.414445
## iter 160 value 135.492289
## iter 170 value 125.738463
## iter 180 value 118.036333
## iter 190 value 110.490285
## iter 200 value 104.562541
## iter 210 value 99.692701
## iter 220 value 94.861440
## iter 230 value 90.734906
## iter 240 value 87.690553
## iter 250 value 84.938820
## iter 260 value 82.629609
## iter 270 value 78.976927
## iter 280 value 76.655006
## iter 290 value 74.724031
## iter 300 value 70.914051
## iter 310 value 66.237798
## iter 320 value 63.612419
## iter 330 value 61.827949
## iter 340 value 59.937911
## iter 350 value 58.045567
## iter 360 value 56.209759
## iter 370 value 54.309045
## iter 380 value 53.266239
## iter 390 value 52.358559
## iter 400 value 51.581962
## iter 410 value 50.605355
## iter 420 value 49.724843
## iter 430 value 48.713986
## iter 440 value 47.803062
## iter 450 value 47.197608
## iter 460 value 46.564369
## iter 470 value 46.010227
## iter 480 value 45.543231
## iter 490 value 45.250383
## iter 500 value 45.190447
## final value 45.190447
## stopped after 500 iterations
## # weights: 25
## initial value 1372476.830600
## iter 10 value 66071.850729
## iter 20 value 16301.884162
## iter 30 value 15924.138647
## iter 40 value 15669.361705
## iter 50 value 15504.252382
## iter 60 value 14128.677558
## iter 70 value 12415.066884
## iter 80 value 11731.682723
## iter 90 value 11597.027862
## iter 100 value 11542.573502
## iter 110 value 11516.507493
## final value 11516.361465
## converged
## # weights: 61
## initial value 1407938.264648
## iter 10 value 2843.758487
## iter 20 value 1843.940204
## iter 30 value 1205.152712
## iter 40 value 960.451207
## iter 50 value 866.733606
## iter 60 value 752.674082
## iter 70 value 726.720860
## iter 80 value 720.863376
## iter 90 value 717.945696
## iter 100 value 715.769818
## iter 110 value 691.123577
## iter 120 value 668.661251
## iter 130 value 663.267880
## iter 140 value 661.586601
## iter 150 value 659.652976
## iter 160 value 656.104034
## iter 170 value 647.580423
## iter 180 value 636.526074
## iter 190 value 620.068787
## iter 200 value 609.319300
## iter 210 value 601.334325
## iter 220 value 599.195914
## iter 230 value 598.074594
## iter 240 value 596.885061
## iter 250 value 595.768566
## iter 260 value 595.725856
## iter 270 value 595.583514
## iter 280 value 595.257327
## iter 290 value 595.038863
## iter 300 value 593.962638
## iter 310 value 592.675694
## iter 320 value 591.742046
## iter 330 value 591.226827
## iter 340 value 590.903815
## iter 350 value 590.603719
## iter 360 value 590.340362
## iter 370 value 590.029252
## iter 380 value 589.625512
## iter 390 value 589.568694
## iter 400 value 587.965804
## iter 410 value 585.626207
## iter 420 value 585.403580
## iter 430 value 585.329958
## iter 440 value 585.152390
## iter 450 value 584.868036
## iter 460 value 584.553890
## iter 470 value 584.402547
## iter 480 value 584.398097
## iter 490 value 584.299566
## iter 500 value 583.819417
## final value 583.819417
## stopped after 500 iterations
## # weights: 121
## initial value 1400959.315384
## iter 10 value 3680.038454
## iter 20 value 1607.147930
## iter 30 value 1149.990065
## iter 40 value 880.060931
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## final value 433.568055
## stopped after 500 iterations
## # weights: 181
## initial value 1436380.947420
## iter 10 value 1396.649747
## iter 20 value 780.122467
## iter 30 value 624.376353
## iter 40 value 498.828115
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## iter 480 value 189.502272
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## iter 500 value 188.874484
## final value 188.874484
## stopped after 500 iterations
## # weights: 241
## initial value 1361337.236566
## iter 10 value 1219.972364
## iter 20 value 853.936547
## iter 30 value 638.681809
## iter 40 value 510.339447
## iter 50 value 429.476184
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## iter 70 value 294.870702
## iter 80 value 252.298186
## iter 90 value 217.298982
## iter 100 value 185.503750
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## iter 470 value 37.506902
## iter 480 value 37.394181
## iter 490 value 37.358919
## iter 500 value 37.349176
## final value 37.349176
## stopped after 500 iterations
## # weights: 25
## initial value 1387099.627384
## iter 10 value 6884.863381
## iter 20 value 5742.708364
## iter 30 value 5648.924554
## iter 40 value 5489.348356
## iter 50 value 5294.227643
## iter 60 value 4191.619062
## iter 70 value 3439.820488
## iter 80 value 1865.159081
## iter 90 value 1354.174023
## iter 100 value 1230.275976
## iter 110 value 1213.606377
## iter 120 value 1203.457053
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## iter 180 value 1133.782683
## iter 190 value 1115.050236
## iter 200 value 1098.741020
## iter 210 value 1071.941371
## iter 220 value 1069.565696
## iter 230 value 1068.972566
## iter 240 value 1065.824168
## iter 250 value 1063.384913
## iter 260 value 1061.492019
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## iter 320 value 1058.548991
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## iter 370 value 1057.265463
## iter 380 value 1057.261163
## iter 380 value 1057.261161
## iter 380 value 1057.261157
## final value 1057.261157
## converged
## # weights: 61
## initial value 1409061.029459
## iter 10 value 1454.501398
## iter 20 value 1173.662421
## iter 30 value 948.423994
## iter 40 value 818.321733
## iter 50 value 723.675688
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## iter 90 value 612.884284
## iter 100 value 610.410488
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## iter 180 value 593.442563
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## iter 210 value 585.790198
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## iter 230 value 582.950025
## iter 240 value 582.386864
## iter 250 value 582.116854
## iter 260 value 582.086040
## iter 270 value 581.971393
## iter 280 value 581.690192
## iter 290 value 581.460932
## iter 300 value 581.353291
## iter 310 value 581.149949
## iter 320 value 580.848503
## iter 330 value 580.611379
## iter 340 value 580.393534
## iter 350 value 580.266394
## iter 360 value 580.141481
## iter 370 value 579.957066
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## iter 450 value 578.975600
## iter 460 value 578.868404
## iter 470 value 578.775307
## iter 480 value 578.687300
## iter 490 value 578.665848
## final value 578.663563
## converged
## # weights: 121
## initial value 1416636.374166
## iter 10 value 1671.478236
## iter 20 value 1000.629062
## iter 30 value 821.121997
## iter 40 value 713.814097
## iter 50 value 641.158097
## iter 60 value 608.680645
## iter 70 value 586.740869
## iter 80 value 567.069916
## iter 90 value 553.661699
## iter 100 value 545.983177
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## iter 140 value 527.976666
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## iter 180 value 487.147887
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## iter 200 value 454.176554
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## iter 220 value 438.455061
## iter 230 value 430.930929
## iter 240 value 415.480240
## iter 250 value 394.001123
## iter 260 value 387.039195
## iter 270 value 384.757568
## iter 280 value 382.687809
## iter 290 value 379.690969
## iter 300 value 378.106648
## iter 310 value 377.187713
## iter 320 value 375.975369
## iter 330 value 374.850924
## iter 340 value 374.210535
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## iter 380 value 373.393873
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## iter 400 value 373.367752
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## iter 420 value 373.338195
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## iter 460 value 373.258294
## iter 470 value 372.411457
## iter 480 value 371.348802
## iter 490 value 370.902117
## iter 500 value 370.609300
## final value 370.609300
## stopped after 500 iterations
## # weights: 181
## initial value 1381719.005892
## iter 10 value 1467.172498
## iter 20 value 786.635499
## iter 30 value 620.697677
## iter 40 value 515.593629
## iter 50 value 431.749592
## iter 60 value 362.889480
## iter 70 value 319.911837
## iter 80 value 275.422756
## iter 90 value 248.714420
## iter 100 value 230.661755
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## iter 180 value 134.984710
## iter 190 value 128.138283
## iter 200 value 123.781473
## iter 210 value 120.680937
## iter 220 value 116.928077
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## iter 240 value 112.195092
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## iter 500 value 98.901575
## final value 98.901575
## stopped after 500 iterations
## # weights: 241
## initial value 1406575.679480
## iter 10 value 1475.842726
## iter 20 value 810.414268
## iter 30 value 656.808281
## iter 40 value 565.655535
## iter 50 value 446.121098
## iter 60 value 353.930487
## iter 70 value 297.073127
## iter 80 value 264.388584
## iter 90 value 236.573331
## iter 100 value 202.989764
## iter 110 value 183.267716
## iter 120 value 169.714214
## iter 130 value 160.201311
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## iter 150 value 144.941023
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## iter 180 value 129.112631
## iter 190 value 121.705868
## iter 200 value 114.814578
## iter 210 value 109.074391
## iter 220 value 104.472022
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## iter 250 value 94.504699
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## iter 310 value 84.627525
## iter 320 value 83.374603
## iter 330 value 81.261878
## iter 340 value 79.186915
## iter 350 value 78.093975
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## iter 370 value 74.926649
## iter 380 value 72.850564
## iter 390 value 71.190179
## iter 400 value 69.651247
## iter 410 value 68.466478
## iter 420 value 67.145333
## iter 430 value 65.799797
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## iter 470 value 61.325149
## iter 480 value 60.164964
## iter 490 value 59.507512
## iter 500 value 59.326190
## final value 59.326190
## stopped after 500 iterations
## # weights: 25
## initial value 1383669.468917
## iter 10 value 19610.500507
## iter 20 value 14471.155774
## iter 30 value 10709.210303
## iter 40 value 10191.043565
## iter 50 value 6046.932930
## iter 60 value 4952.313284
## iter 70 value 3729.353329
## iter 80 value 2648.268330
## iter 90 value 2088.731714
## iter 100 value 1884.026272
## iter 110 value 1676.235137
## iter 120 value 1401.763556
## iter 130 value 1364.790876
## iter 140 value 1355.966270
## iter 150 value 1352.899702
## iter 160 value 1334.776159
## iter 170 value 1315.688009
## iter 180 value 1311.984388
## iter 190 value 1311.759084
## iter 200 value 1311.742942
## iter 210 value 1311.607034
## final value 1311.605220
## converged
## # weights: 61
## initial value 1386502.060133
## iter 10 value 9740.599426
## iter 20 value 5854.369773
## iter 30 value 4467.394927
## iter 40 value 3972.388546
## iter 50 value 2908.281894
## iter 60 value 1997.864900
## iter 70 value 1432.078601
## iter 80 value 1093.070390
## iter 90 value 925.424098
## iter 100 value 848.214016
## iter 110 value 825.406624
## iter 120 value 813.197770
## iter 130 value 803.037929
## iter 140 value 797.717340
## iter 150 value 796.269914
## iter 160 value 795.568900
## iter 170 value 795.506408
## iter 180 value 795.359322
## iter 190 value 795.229246
## iter 200 value 795.167448
## final value 795.163462
## converged
## # weights: 121
## initial value 1413503.513575
## iter 10 value 3403.502379
## iter 20 value 1782.465199
## iter 30 value 1394.351447
## iter 40 value 1237.435546
## iter 50 value 1143.684364
## iter 60 value 1095.687901
## iter 70 value 980.102309
## iter 80 value 804.775659
## iter 90 value 727.451692
## iter 100 value 649.523058
## iter 110 value 605.151559
## iter 120 value 581.368095
## iter 130 value 560.948501
## iter 140 value 538.796003
## iter 150 value 525.258830
## iter 160 value 518.871315
## iter 170 value 512.737274
## iter 180 value 510.199211
## iter 190 value 507.535789
## iter 200 value 498.924478
## iter 210 value 492.578750
## iter 220 value 489.662353
## iter 230 value 484.872188
## iter 240 value 477.891235
## iter 250 value 473.021551
## iter 260 value 470.758371
## iter 270 value 468.759570
## iter 280 value 462.677149
## iter 290 value 458.149118
## iter 300 value 455.809965
## iter 310 value 455.062626
## iter 320 value 453.919139
## iter 330 value 452.168341
## iter 340 value 450.792724
## iter 350 value 448.140931
## iter 360 value 445.935086
## iter 370 value 444.882889
## iter 380 value 442.779359
## iter 390 value 441.208245
## iter 400 value 439.674456
## iter 410 value 437.653320
## iter 420 value 436.556541
## iter 430 value 436.216592
## iter 440 value 436.143452
## iter 450 value 436.129581
## iter 460 value 436.129288
## final value 436.129259
## converged
## # weights: 181
## initial value 1341344.982892
## iter 10 value 1178.530156
## iter 20 value 857.914268
## iter 30 value 716.172612
## iter 40 value 585.632401
## iter 50 value 517.967504
## iter 60 value 491.344751
## iter 70 value 453.233898
## iter 80 value 434.577200
## iter 90 value 419.083770
## iter 100 value 402.736674
## iter 110 value 391.811097
## iter 120 value 380.858834
## iter 130 value 373.391089
## iter 140 value 366.968491
## iter 150 value 364.547728
## iter 160 value 363.011272
## iter 170 value 362.093043
## iter 180 value 361.032721
## iter 190 value 359.115655
## iter 200 value 356.567070
## iter 210 value 353.191551
## iter 220 value 349.966540
## iter 230 value 347.862241
## iter 240 value 345.343848
## iter 250 value 343.590459
## iter 260 value 342.088736
## iter 270 value 340.313085
## iter 280 value 339.170779
## iter 290 value 337.381680
## iter 300 value 336.310066
## iter 310 value 335.823987
## iter 320 value 335.300823
## iter 330 value 334.938002
## iter 340 value 334.610412
## iter 350 value 334.047338
## iter 360 value 331.861163
## iter 370 value 331.408506
## iter 380 value 330.989204
## iter 390 value 330.317282
## iter 400 value 329.960552
## iter 410 value 329.811912
## iter 420 value 329.626049
## iter 430 value 329.559200
## iter 440 value 329.405436
## iter 450 value 328.916151
## iter 460 value 328.223767
## iter 470 value 327.861521
## iter 480 value 327.777054
## iter 490 value 327.729097
## iter 500 value 327.714027
## final value 327.714027
## stopped after 500 iterations
## # weights: 241
## initial value 1423336.286128
## iter 10 value 1851.926684
## iter 20 value 1016.069858
## iter 30 value 876.867018
## iter 40 value 713.671169
## iter 50 value 644.062703
## iter 60 value 605.523475
## iter 70 value 572.504108
## iter 80 value 546.979620
## iter 90 value 512.908416
## iter 100 value 490.775363
## iter 110 value 480.697795
## iter 120 value 472.127756
## iter 130 value 465.530078
## iter 140 value 460.845053
## iter 150 value 455.078009
## iter 160 value 450.433695
## iter 170 value 446.406496
## iter 180 value 443.244537
## iter 190 value 439.943566
## iter 200 value 437.041391
## iter 210 value 434.357852
## iter 220 value 431.906909
## iter 230 value 429.154806
## iter 240 value 421.561884
## iter 250 value 416.313930
## iter 260 value 408.862144
## iter 270 value 403.088491
## iter 280 value 395.989093
## iter 290 value 387.612911
## iter 300 value 380.353348
## iter 310 value 376.616421
## iter 320 value 372.914839
## iter 330 value 369.645424
## iter 340 value 367.133096
## iter 350 value 364.126654
## iter 360 value 361.527724
## iter 370 value 359.091793
## iter 380 value 356.398288
## iter 390 value 354.341430
## iter 400 value 352.029597
## iter 410 value 349.831360
## iter 420 value 347.505267
## iter 430 value 344.644129
## iter 440 value 342.933698
## iter 450 value 340.715338
## iter 460 value 338.575646
## iter 470 value 335.804255
## iter 480 value 334.177821
## iter 490 value 333.308001
## iter 500 value 332.497097
## final value 332.497097
## stopped after 500 iterations
## # weights: 25
## initial value 1375188.420772
## iter 10 value 16119.511031
## iter 20 value 15880.421201
## iter 30 value 15062.229959
## iter 40 value 13267.068718
## iter 50 value 11659.520583
## iter 60 value 11489.160385
## iter 70 value 11184.906299
## iter 80 value 11021.728167
## iter 90 value 9136.261094
## iter 100 value 7171.549235
## iter 110 value 3412.599879
## iter 120 value 3087.397655
## iter 130 value 2761.127256
## iter 140 value 1604.437857
## iter 150 value 1118.981524
## iter 160 value 1049.019514
## iter 170 value 1029.997241
## iter 180 value 1028.590950
## iter 190 value 1022.557111
## iter 200 value 1012.277231
## iter 210 value 1010.768156
## iter 220 value 997.385867
## iter 230 value 995.430417
## iter 240 value 991.860910
## iter 250 value 967.457156
## iter 260 value 966.615710
## iter 270 value 966.350953
## iter 280 value 966.341595
## iter 290 value 962.880400
## iter 300 value 962.087847
## iter 310 value 956.257927
## iter 320 value 911.079431
## iter 330 value 902.345855
## iter 340 value 896.786485
## iter 350 value 896.011580
## iter 360 value 894.410254
## iter 370 value 887.790183
## iter 380 value 886.482263
## iter 390 value 886.234790
## iter 400 value 886.138814
## iter 410 value 886.073346
## iter 420 value 883.219830
## iter 430 value 872.502834
## iter 440 value 851.912182
## iter 450 value 845.185184
## iter 460 value 835.965056
## iter 470 value 824.266305
## iter 480 value 819.949615
## iter 490 value 818.219794
## iter 500 value 817.865796
## final value 817.865796
## stopped after 500 iterations
## # weights: 61
## initial value 1399511.976977
## iter 10 value 3294.217753
## iter 20 value 1040.022472
## iter 30 value 864.268705
## iter 40 value 792.876915
## iter 50 value 732.249784
## iter 60 value 696.854187
## iter 70 value 657.797294
## iter 80 value 633.222310
## iter 90 value 622.959040
## iter 100 value 606.550763
## iter 110 value 595.308315
## iter 120 value 585.281760
## iter 130 value 582.658232
## iter 140 value 580.469537
## iter 150 value 577.411224
## iter 160 value 573.015636
## iter 170 value 568.183093
## iter 180 value 563.184305
## iter 190 value 557.760849
## iter 200 value 556.321240
## iter 210 value 553.737665
## iter 220 value 551.685934
## iter 230 value 550.088426
## iter 240 value 549.774181
## iter 250 value 549.538638
## iter 260 value 549.521831
## iter 270 value 549.460909
## iter 280 value 549.130537
## iter 290 value 548.268939
## iter 300 value 547.965162
## iter 310 value 547.647210
## iter 320 value 547.448586
## iter 330 value 547.394297
## iter 340 value 547.375884
## iter 350 value 547.372380
## iter 360 value 547.370778
## final value 547.370379
## converged
## # weights: 121
## initial value 1318817.049339
## iter 10 value 1453.391468
## iter 20 value 921.729845
## iter 30 value 703.977163
## iter 40 value 593.417066
## iter 50 value 550.198590
## iter 60 value 505.727508
## iter 70 value 468.703416
## iter 80 value 443.217625
## iter 90 value 424.657523
## iter 100 value 403.152947
## iter 110 value 383.859944
## iter 120 value 372.970880
## iter 130 value 364.821421
## iter 140 value 354.805915
## iter 150 value 349.642399
## iter 160 value 344.135042
## iter 170 value 340.856247
## iter 180 value 339.335419
## iter 190 value 337.513490
## iter 200 value 335.723795
## iter 210 value 334.423805
## iter 220 value 333.553679
## iter 230 value 331.104395
## iter 240 value 328.718307
## iter 250 value 328.180533
## iter 260 value 327.685124
## iter 270 value 326.494534
## iter 280 value 325.364988
## iter 290 value 324.406136
## iter 300 value 322.301884
## iter 310 value 320.781151
## iter 320 value 318.420135
## iter 330 value 316.643753
## iter 340 value 314.956362
## iter 350 value 313.183301
## iter 360 value 311.139530
## iter 370 value 310.386860
## iter 380 value 309.764422
## iter 390 value 308.243278
## iter 400 value 308.018837
## iter 410 value 307.967262
## iter 420 value 307.952379
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## iter 470 value 307.883103
## iter 480 value 307.882997
## iter 480 value 307.882995
## iter 480 value 307.882995
## final value 307.882995
## converged
## # weights: 181
## initial value 1419080.413303
## iter 10 value 1100.036839
## iter 20 value 764.218444
## iter 30 value 628.233927
## iter 40 value 496.085788
## iter 50 value 417.574751
## iter 60 value 378.683439
## iter 70 value 317.964373
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## iter 90 value 246.929342
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## iter 470 value 123.890380
## iter 480 value 123.030975
## iter 490 value 122.161475
## iter 500 value 121.987209
## final value 121.987209
## stopped after 500 iterations
## # weights: 241
## initial value 1397038.290086
## iter 10 value 1211.378573
## iter 20 value 773.976683
## iter 30 value 590.106615
## iter 40 value 447.123158
## iter 50 value 356.949966
## iter 60 value 317.098232
## iter 70 value 282.110668
## iter 80 value 253.652867
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## iter 100 value 210.181163
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## iter 420 value 73.509443
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## iter 470 value 71.591822
## iter 480 value 71.265800
## iter 490 value 71.136214
## iter 500 value 71.103190
## final value 71.103190
## stopped after 500 iterations
## # weights: 25
## initial value 1403525.145478
## iter 10 value 21688.413349
## iter 20 value 18074.027468
## iter 30 value 13253.667308
## iter 40 value 5355.576130
## iter 50 value 4198.805374
## iter 60 value 2884.583194
## iter 70 value 1664.628362
## iter 80 value 1285.733297
## iter 90 value 1216.410422
## iter 100 value 1197.994005
## iter 110 value 1144.330453
## iter 120 value 1108.928339
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## iter 210 value 937.173331
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## iter 230 value 929.316788
## iter 240 value 928.531134
## iter 250 value 928.530112
## iter 260 value 928.524564
## final value 928.524492
## converged
## # weights: 61
## initial value 1366580.530735
## iter 10 value 4025.099451
## iter 20 value 3381.118706
## iter 30 value 3067.156812
## iter 40 value 2276.477332
## iter 50 value 1789.455966
## iter 60 value 1412.358155
## iter 70 value 1180.183655
## iter 80 value 1128.216822
## iter 90 value 1108.572847
## iter 100 value 1095.400367
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## iter 130 value 1078.936110
## iter 140 value 1078.142800
## iter 150 value 1075.182631
## iter 160 value 1072.198550
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## iter 200 value 1064.645319
## iter 210 value 1063.811087
## iter 220 value 1059.156459
## iter 230 value 958.537170
## iter 240 value 835.448039
## iter 250 value 768.284908
## iter 260 value 759.983483
## iter 270 value 757.164801
## iter 280 value 756.291263
## iter 290 value 756.122870
## iter 300 value 754.684904
## iter 310 value 753.736673
## iter 320 value 748.001538
## iter 330 value 747.739831
## iter 340 value 746.181934
## iter 350 value 742.723602
## iter 360 value 738.673894
## iter 370 value 737.319722
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## iter 400 value 734.068286
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## iter 420 value 733.678072
## iter 430 value 731.830443
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## iter 450 value 729.025183
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## iter 470 value 728.760947
## iter 480 value 728.617584
## iter 490 value 728.443569
## iter 500 value 728.165814
## final value 728.165814
## stopped after 500 iterations
## # weights: 121
## initial value 1428452.421636
## iter 10 value 6383.787906
## iter 20 value 2051.752399
## iter 30 value 1125.636217
## iter 40 value 874.564366
## iter 50 value 691.287940
## iter 60 value 590.589031
## iter 70 value 531.690486
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## iter 90 value 441.176574
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## iter 200 value 328.019198
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## iter 270 value 310.587679
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## iter 300 value 299.562820
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## iter 320 value 291.253364
## iter 330 value 290.269132
## iter 340 value 289.006626
## iter 350 value 287.202325
## iter 360 value 285.613001
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## iter 440 value 281.347235
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## iter 470 value 281.117442
## iter 480 value 281.056637
## iter 490 value 281.021164
## iter 500 value 281.019280
## final value 281.019280
## stopped after 500 iterations
## # weights: 181
## initial value 1404235.414039
## iter 10 value 1033.468879
## iter 20 value 732.040510
## iter 30 value 591.746626
## iter 40 value 449.667585
## iter 50 value 378.525598
## iter 60 value 339.014724
## iter 70 value 295.532887
## iter 80 value 263.315277
## iter 90 value 243.927583
## iter 100 value 225.755741
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## iter 480 value 92.393109
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## iter 500 value 91.597290
## final value 91.597290
## stopped after 500 iterations
## # weights: 241
## initial value 1297007.387561
## iter 10 value 1363.616777
## iter 20 value 845.703911
## iter 30 value 619.801324
## iter 40 value 507.371582
## iter 50 value 383.606302
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## iter 180 value 118.442321
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## iter 210 value 101.169381
## iter 220 value 94.170370
## iter 230 value 89.445134
## iter 240 value 86.063176
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## iter 300 value 73.833694
## iter 310 value 71.926612
## iter 320 value 69.963229
## iter 330 value 68.019065
## iter 340 value 66.204908
## iter 350 value 63.593963
## iter 360 value 61.011151
## iter 370 value 58.862093
## iter 380 value 55.888522
## iter 390 value 53.943839
## iter 400 value 52.251507
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## iter 420 value 50.005017
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## iter 480 value 45.125349
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## iter 500 value 44.956544
## final value 44.956544
## stopped after 500 iterations
## # weights: 25
## initial value 1401352.810833
## iter 10 value 22584.803653
## iter 20 value 12571.827010
## iter 30 value 10999.636648
## iter 40 value 9299.545654
## iter 50 value 8684.218053
## iter 60 value 8546.295431
## iter 70 value 8479.953727
## final value 8479.420862
## converged
## # weights: 61
## initial value 1390331.906384
## iter 10 value 32521.360425
## iter 20 value 15876.235844
## iter 30 value 8350.475469
## iter 40 value 4937.028221
## iter 50 value 4263.014694
## iter 60 value 3876.746316
## iter 70 value 3580.612860
## iter 80 value 3502.297249
## iter 90 value 3441.276382
## iter 100 value 3330.367434
## iter 110 value 2999.424938
## iter 120 value 2562.424333
## iter 130 value 2414.360468
## iter 140 value 2246.654949
## iter 150 value 2124.622361
## iter 160 value 1926.190230
## iter 170 value 1834.007432
## iter 180 value 1774.641306
## iter 190 value 1761.455676
## iter 200 value 1757.333466
## iter 210 value 1757.055421
## iter 220 value 1756.354926
## iter 230 value 1751.533501
## iter 240 value 1751.212528
## iter 250 value 1751.068019
## iter 260 value 1750.968093
## iter 270 value 1750.009293
## iter 280 value 1748.692068
## iter 290 value 1747.650787
## iter 300 value 1747.302558
## iter 310 value 1746.756220
## iter 320 value 1745.150726
## iter 330 value 1744.558327
## iter 340 value 1744.449313
## iter 350 value 1744.253696
## iter 360 value 1744.233701
## iter 370 value 1744.205713
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## iter 390 value 1744.177084
## iter 400 value 1744.001399
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## iter 420 value 1743.833483
## iter 430 value 1743.515871
## iter 440 value 1742.289992
## iter 450 value 1724.985480
## iter 460 value 1670.950687
## iter 470 value 1648.410435
## iter 480 value 1645.706704
## iter 490 value 1643.629749
## iter 500 value 1639.814294
## final value 1639.814294
## stopped after 500 iterations
## # weights: 121
## initial value 1372741.224659
## iter 10 value 1361.814825
## iter 20 value 828.234699
## iter 30 value 693.247544
## iter 40 value 603.718984
## iter 50 value 541.420420
## iter 60 value 463.671061
## iter 70 value 416.380510
## iter 80 value 386.960445
## iter 90 value 365.384481
## iter 100 value 352.246981
## iter 110 value 337.449557
## iter 120 value 325.041891
## iter 130 value 306.198126
## iter 140 value 291.685324
## iter 150 value 284.360799
## iter 160 value 274.622291
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## iter 210 value 256.351135
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## iter 400 value 198.495495
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## iter 460 value 194.540000
## iter 470 value 194.452072
## iter 480 value 194.402534
## iter 490 value 194.319312
## iter 500 value 194.315074
## final value 194.315074
## stopped after 500 iterations
## # weights: 181
## initial value 1378444.892063
## iter 10 value 1918.274623
## iter 20 value 716.689974
## iter 30 value 530.492845
## iter 40 value 414.374758
## iter 50 value 323.227494
## iter 60 value 274.052553
## iter 70 value 245.623020
## iter 80 value 220.803052
## iter 90 value 201.943922
## iter 100 value 188.866415
## iter 110 value 179.686226
## iter 120 value 171.599604
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## iter 140 value 160.946160
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## iter 470 value 116.217539
## iter 480 value 115.908564
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## iter 500 value 114.932059
## final value 114.932059
## stopped after 500 iterations
## # weights: 241
## initial value 1365942.690459
## iter 10 value 1202.714914
## iter 20 value 756.406976
## iter 30 value 640.140190
## iter 40 value 540.410517
## iter 50 value 440.159302
## iter 60 value 374.215397
## iter 70 value 341.029373
## iter 80 value 285.824140
## iter 90 value 234.919689
## iter 100 value 205.594173
## iter 110 value 184.834819
## iter 120 value 171.595806
## iter 130 value 161.194909
## iter 140 value 153.437564
## iter 150 value 147.235293
## iter 160 value 139.551090
## iter 170 value 129.336066
## iter 180 value 120.819066
## iter 190 value 113.383884
## iter 200 value 107.460461
## iter 210 value 101.088138
## iter 220 value 91.803439
## iter 230 value 83.650749
## iter 240 value 77.978655
## iter 250 value 74.024840
## iter 260 value 71.923859
## iter 270 value 69.464531
## iter 280 value 66.337318
## iter 290 value 63.852183
## iter 300 value 60.344949
## iter 310 value 57.002596
## iter 320 value 54.349392
## iter 330 value 52.151034
## iter 340 value 49.925042
## iter 350 value 48.017476
## iter 360 value 46.812591
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## iter 380 value 44.881476
## iter 390 value 44.417924
## iter 400 value 44.086062
## iter 410 value 43.879743
## iter 420 value 43.703380
## iter 430 value 43.501966
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## iter 470 value 42.853705
## iter 480 value 42.709676
## iter 490 value 42.654831
## iter 500 value 42.641270
## final value 42.641270
## stopped after 500 iterations
## # weights: 25
## initial value 1369311.602739
## iter 10 value 6820.461951
## iter 20 value 5446.843389
## iter 30 value 5295.517703
## iter 40 value 4906.365573
## iter 50 value 4273.899997
## iter 60 value 3727.361713
## iter 70 value 1766.238133
## iter 80 value 1402.899845
## iter 90 value 1346.814811
## iter 100 value 1328.122741
## iter 110 value 1301.694360
## iter 120 value 1287.661388
## iter 130 value 1282.648408
## iter 140 value 1280.563819
## iter 150 value 1278.920853
## iter 160 value 1278.912377
## final value 1278.911363
## converged
## # weights: 61
## initial value 1377040.387857
## iter 10 value 8571.316210
## iter 20 value 6387.743023
## iter 30 value 5753.483085
## iter 40 value 5440.730456
## iter 50 value 4714.881211
## iter 60 value 3345.592322
## iter 70 value 2573.088092
## iter 80 value 2317.392581
## iter 90 value 2283.694066
## iter 100 value 2258.226956
## iter 110 value 2225.707491
## iter 120 value 2201.629906
## iter 130 value 2176.421395
## iter 140 value 2137.332954
## iter 150 value 2121.353478
## iter 160 value 2100.527564
## iter 170 value 2000.596293
## iter 180 value 1818.544666
## iter 190 value 1789.426629
## iter 200 value 1787.530658
## iter 210 value 1773.212944
## iter 220 value 1760.690107
## iter 230 value 1749.447440
## iter 240 value 1746.319286
## iter 250 value 1745.545835
## iter 260 value 1744.931662
## iter 270 value 1744.452723
## iter 280 value 1743.533941
## iter 290 value 1717.677648
## iter 300 value 1698.646441
## iter 310 value 1652.563573
## iter 320 value 1644.484688
## iter 330 value 1642.039126
## iter 340 value 1622.964985
## iter 350 value 1603.421679
## iter 360 value 1583.012274
## iter 370 value 1563.759331
## iter 380 value 1512.874144
## iter 390 value 1348.064613
## iter 400 value 1019.306111
## iter 410 value 952.625567
## iter 420 value 912.827907
## iter 430 value 898.958324
## iter 440 value 892.346428
## iter 450 value 868.011342
## iter 460 value 865.222298
## iter 470 value 854.609702
## iter 480 value 848.468739
## iter 490 value 848.260851
## iter 500 value 844.915794
## final value 844.915794
## stopped after 500 iterations
## # weights: 121
## initial value 1426097.473036
## iter 10 value 3019.134982
## iter 20 value 1534.369030
## iter 30 value 1048.182910
## iter 40 value 762.735738
## iter 50 value 650.644900
## iter 60 value 591.275378
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## iter 90 value 516.034614
## iter 100 value 491.111659
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## iter 170 value 387.683424
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## iter 200 value 339.449921
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## iter 290 value 300.125644
## iter 300 value 298.034447
## iter 310 value 295.206864
## iter 320 value 291.522073
## iter 330 value 285.937588
## iter 340 value 280.633593
## iter 350 value 274.581599
## iter 360 value 269.913173
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## iter 380 value 267.271312
## iter 390 value 266.459392
## iter 400 value 265.622675
## iter 410 value 265.390142
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## iter 460 value 264.807567
## iter 470 value 264.470707
## iter 480 value 264.101405
## iter 490 value 263.905791
## iter 500 value 263.901083
## final value 263.901083
## stopped after 500 iterations
## # weights: 181
## initial value 1386961.737673
## iter 10 value 1078.049854
## iter 20 value 742.927129
## iter 30 value 636.335191
## iter 40 value 520.049415
## iter 50 value 415.406605
## iter 60 value 363.600726
## iter 70 value 322.840754
## iter 80 value 288.649996
## iter 90 value 259.843976
## iter 100 value 241.239006
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## iter 360 value 119.802849
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## iter 380 value 119.317299
## iter 390 value 119.151819
## iter 400 value 118.567606
## iter 410 value 117.932442
## iter 420 value 117.282406
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## iter 450 value 113.857906
## iter 460 value 112.988796
## iter 470 value 112.437231
## iter 480 value 111.998950
## iter 490 value 111.572933
## iter 500 value 110.859297
## final value 110.859297
## stopped after 500 iterations
## # weights: 241
## initial value 1330168.016794
## iter 10 value 1354.057588
## iter 20 value 735.288960
## iter 30 value 597.233265
## iter 40 value 461.802342
## iter 50 value 359.972501
## iter 60 value 289.409395
## iter 70 value 242.612727
## iter 80 value 209.506249
## iter 90 value 186.474628
## iter 100 value 169.072803
## iter 110 value 153.979989
## iter 120 value 141.134093
## iter 130 value 127.000045
## iter 140 value 116.141903
## iter 150 value 109.717238
## iter 160 value 104.958974
## iter 170 value 97.674274
## iter 180 value 91.368282
## iter 190 value 85.742121
## iter 200 value 81.929063
## iter 210 value 78.274056
## iter 220 value 74.237042
## iter 230 value 67.501049
## iter 240 value 62.110533
## iter 250 value 58.471807
## iter 260 value 54.540970
## iter 270 value 50.742982
## iter 280 value 47.718846
## iter 290 value 45.321280
## iter 300 value 43.246547
## iter 310 value 41.025847
## iter 320 value 38.058901
## iter 330 value 35.478419
## iter 340 value 33.212874
## iter 350 value 30.464264
## iter 360 value 28.765378
## iter 370 value 27.351663
## iter 380 value 25.782100
## iter 390 value 24.665799
## iter 400 value 23.769805
## iter 410 value 23.257431
## iter 420 value 22.891011
## iter 430 value 22.369908
## iter 440 value 21.935227
## iter 450 value 21.638614
## iter 460 value 21.338875
## iter 470 value 20.627857
## iter 480 value 20.050029
## iter 490 value 19.814863
## iter 500 value 19.753910
## final value 19.753910
## stopped after 500 iterations
## # weights: 25
## initial value 1356805.638731
## iter 10 value 75264.400144
## iter 20 value 27260.137525
## iter 30 value 10308.660240
## iter 40 value 7848.320484
## iter 50 value 6828.745253
## iter 60 value 5670.820930
## iter 70 value 4079.676668
## iter 80 value 3184.411883
## iter 90 value 2451.642550
## iter 100 value 1872.174980
## iter 110 value 1696.033481
## iter 120 value 1660.900048
## iter 130 value 1632.964370
## iter 140 value 1573.871316
## iter 150 value 1482.148184
## iter 160 value 1478.769317
## iter 170 value 1478.278132
## iter 180 value 1477.975241
## iter 190 value 1476.658226
## final value 1476.608604
## converged
## # weights: 61
## initial value 1366095.037300
## iter 10 value 15500.230585
## iter 20 value 6932.150124
## iter 30 value 5246.628694
## iter 40 value 4242.189076
## iter 50 value 3329.164679
## iter 60 value 2366.227537
## iter 70 value 1708.938104
## iter 80 value 1391.636327
## iter 90 value 1232.280619
## iter 100 value 1118.446332
## iter 110 value 1055.573254
## iter 120 value 1039.652446
## iter 130 value 1029.778817
## iter 140 value 1013.329635
## iter 150 value 965.409263
## iter 160 value 930.003816
## iter 170 value 898.710952
## iter 180 value 865.355531
## iter 190 value 819.846356
## iter 200 value 782.378552
## iter 210 value 763.745785
## iter 220 value 745.942866
## iter 230 value 735.137048
## iter 240 value 726.138018
## iter 250 value 722.721128
## iter 260 value 721.120291
## iter 270 value 718.594266
## iter 280 value 715.903850
## iter 290 value 714.092672
## iter 300 value 713.335423
## iter 310 value 713.095898
## final value 713.071163
## converged
## # weights: 121
## initial value 1409075.898309
## iter 10 value 1847.817905
## iter 20 value 1068.200220
## iter 30 value 893.294344
## iter 40 value 826.486051
## iter 50 value 762.860846
## iter 60 value 717.960527
## iter 70 value 680.295368
## iter 80 value 639.636964
## iter 90 value 610.556957
## iter 100 value 588.675552
## iter 110 value 574.373950
## iter 120 value 558.432178
## iter 130 value 538.771954
## iter 140 value 525.489361
## iter 150 value 517.690046
## iter 160 value 512.989313
## iter 170 value 506.994262
## iter 180 value 502.948438
## iter 190 value 499.307031
## iter 200 value 497.387117
## iter 210 value 495.900498
## iter 220 value 494.245553
## iter 230 value 492.500804
## iter 240 value 491.115169
## iter 250 value 490.250983
## iter 260 value 489.864965
## iter 270 value 489.276606
## iter 280 value 489.005309
## iter 290 value 488.765986
## iter 300 value 488.709992
## iter 310 value 488.689976
## iter 320 value 488.679460
## iter 330 value 488.673236
## iter 340 value 488.518832
## iter 350 value 488.160426
## iter 360 value 488.092817
## iter 370 value 488.081131
## final value 488.081013
## converged
## # weights: 181
## initial value 1420846.970810
## iter 10 value 1677.569524
## iter 20 value 926.712207
## iter 30 value 722.281281
## iter 40 value 645.123054
## iter 50 value 563.440128
## iter 60 value 529.305134
## iter 70 value 516.971043
## iter 80 value 507.290514
## iter 90 value 496.210461
## iter 100 value 486.665287
## iter 110 value 479.418876
## iter 120 value 474.656296
## iter 130 value 468.756272
## iter 140 value 458.049033
## iter 150 value 448.012822
## iter 160 value 437.367768
## iter 170 value 427.826989
## iter 180 value 421.223473
## iter 190 value 418.060853
## iter 200 value 415.888567
## iter 210 value 411.772442
## iter 220 value 407.892739
## iter 230 value 405.791094
## iter 240 value 403.604615
## iter 250 value 402.023665
## iter 260 value 400.749612
## iter 270 value 399.811389
## iter 280 value 399.011901
## iter 290 value 398.163161
## iter 300 value 397.129480
## iter 310 value 396.220651
## iter 320 value 395.269558
## iter 330 value 394.567892
## iter 340 value 393.845009
## iter 350 value 393.280812
## iter 360 value 392.912667
## iter 370 value 392.607859
## iter 380 value 392.233342
## iter 390 value 391.642458
## iter 400 value 391.226363
## iter 410 value 390.869277
## iter 420 value 390.507116
## iter 430 value 390.241278
## iter 440 value 389.886887
## iter 450 value 388.460695
## iter 460 value 386.689697
## iter 470 value 385.321888
## iter 480 value 383.310046
## iter 490 value 380.175348
## iter 500 value 377.556239
## final value 377.556239
## stopped after 500 iterations
## # weights: 241
## initial value 1440672.319555
## iter 10 value 1623.988543
## iter 20 value 940.362169
## iter 30 value 746.413832
## iter 40 value 640.447421
## iter 50 value 572.005881
## iter 60 value 521.119817
## iter 70 value 488.603134
## iter 80 value 464.232070
## iter 90 value 434.453714
## iter 100 value 417.009765
## iter 110 value 405.767907
## iter 120 value 399.750038
## iter 130 value 393.649568
## iter 140 value 389.200958
## iter 150 value 385.707892
## iter 160 value 382.554259
## iter 170 value 379.918877
## iter 180 value 377.691438
## iter 190 value 374.921612
## iter 200 value 371.379408
## iter 210 value 366.090511
## iter 220 value 363.051108
## iter 230 value 360.440429
## iter 240 value 357.332464
## iter 250 value 354.616328
## iter 260 value 352.067334
## iter 270 value 349.349318
## iter 280 value 345.615171
## iter 290 value 342.425243
## iter 300 value 339.867529
## iter 310 value 338.380519
## iter 320 value 337.175378
## iter 330 value 335.947895
## iter 340 value 334.856446
## iter 350 value 333.704725
## iter 360 value 332.549502
## iter 370 value 332.048847
## iter 380 value 331.756735
## iter 390 value 331.416512
## iter 400 value 330.325096
## iter 410 value 329.015926
## iter 420 value 327.481508
## iter 430 value 326.520416
## iter 440 value 325.972047
## iter 450 value 325.346204
## iter 460 value 324.790732
## iter 470 value 324.278601
## iter 480 value 323.578269
## iter 490 value 323.258868
## iter 500 value 322.740780
## final value 322.740780
## stopped after 500 iterations
## # weights: 25
## initial value 1402172.750021
## iter 10 value 63325.462518
## iter 20 value 17374.865040
## iter 30 value 7851.262685
## iter 40 value 7626.392126
## iter 50 value 6850.267576
## iter 60 value 5566.152060
## iter 70 value 4725.894181
## iter 80 value 3978.485308
## iter 90 value 3969.098548
## iter 100 value 3964.586629
## iter 110 value 3897.822752
## iter 120 value 3875.307228
## iter 130 value 3862.255970
## iter 140 value 3854.882489
## iter 150 value 3510.283214
## iter 160 value 2983.364198
## iter 170 value 2155.151247
## iter 180 value 1733.168315
## iter 190 value 1429.036831
## iter 200 value 1311.264154
## iter 210 value 1303.758465
## iter 220 value 1283.137187
## iter 230 value 1282.673322
## iter 240 value 1213.657112
## iter 250 value 1170.855668
## iter 260 value 1151.766948
## iter 270 value 1147.997427
## iter 280 value 1147.215499
## final value 1147.190345
## converged
## # weights: 61
## initial value 1400054.198890
## iter 10 value 4849.047418
## iter 20 value 1552.762399
## iter 30 value 1225.205040
## iter 40 value 949.977230
## iter 50 value 840.550643
## iter 60 value 793.498010
## iter 70 value 751.506218
## iter 80 value 720.738142
## iter 90 value 696.394507
## iter 100 value 669.001708
## iter 110 value 647.103746
## iter 120 value 628.620741
## iter 130 value 624.584764
## iter 140 value 622.206719
## iter 150 value 617.929340
## iter 160 value 611.620803
## iter 170 value 609.056782
## iter 180 value 595.438423
## iter 190 value 586.192734
## iter 200 value 583.608631
## iter 210 value 581.872718
## iter 220 value 581.102191
## iter 230 value 580.674325
## iter 240 value 580.650733
## iter 250 value 580.603061
## iter 260 value 580.512218
## iter 270 value 579.180102
## iter 280 value 578.353066
## iter 290 value 570.205273
## iter 300 value 565.125652
## iter 310 value 563.558467
## iter 320 value 562.761437
## iter 330 value 562.406119
## iter 340 value 562.302868
## iter 350 value 562.286817
## iter 360 value 562.284577
## iter 370 value 562.277496
## iter 380 value 562.230388
## iter 390 value 562.188212
## iter 400 value 562.174503
## iter 410 value 562.171149
## final value 562.170587
## converged
## # weights: 121
## initial value 1358554.334746
## iter 10 value 1338.043646
## iter 20 value 851.422232
## iter 30 value 709.174966
## iter 40 value 620.502330
## iter 50 value 578.488141
## iter 60 value 533.582756
## iter 70 value 498.647211
## iter 80 value 465.874182
## iter 90 value 436.687549
## iter 100 value 408.599125
## iter 110 value 381.666885
## iter 120 value 360.529755
## iter 130 value 345.929137
## iter 140 value 336.035854
## iter 150 value 323.494814
## iter 160 value 316.865913
## iter 170 value 310.223047
## iter 180 value 304.961407
## iter 190 value 300.516850
## iter 200 value 297.458252
## iter 210 value 295.205302
## iter 220 value 292.894244
## iter 230 value 291.225571
## iter 240 value 290.257770
## iter 250 value 289.800194
## iter 260 value 289.590261
## iter 270 value 289.000019
## iter 280 value 287.961400
## iter 290 value 285.572942
## iter 300 value 279.040888
## iter 310 value 271.858443
## iter 320 value 260.272372
## iter 330 value 250.024629
## iter 340 value 243.181167
## iter 350 value 238.249752
## iter 360 value 235.927820
## iter 370 value 234.576488
## iter 380 value 234.043025
## iter 390 value 232.784801
## iter 400 value 232.382912
## iter 410 value 232.200976
## iter 420 value 232.128865
## iter 430 value 232.041128
## iter 440 value 232.016297
## iter 450 value 232.008511
## iter 460 value 232.006337
## iter 470 value 232.005158
## iter 480 value 232.004205
## final value 232.003724
## converged
## # weights: 181
## initial value 1389840.698867
## iter 10 value 1193.943373
## iter 20 value 774.036560
## iter 30 value 569.949465
## iter 40 value 464.371061
## iter 50 value 381.404003
## iter 60 value 341.305451
## iter 70 value 309.188409
## iter 80 value 270.540829
## iter 90 value 253.057644
## iter 100 value 239.469340
## iter 110 value 228.362532
## iter 120 value 220.170792
## iter 130 value 209.910687
## iter 140 value 195.954325
## iter 150 value 185.858311
## iter 160 value 176.221664
## iter 170 value 169.398743
## iter 180 value 161.459833
## iter 190 value 152.523597
## iter 200 value 145.079997
## iter 210 value 140.144102
## iter 220 value 138.377094
## iter 230 value 136.092874
## iter 240 value 133.733048
## iter 250 value 132.113689
## iter 260 value 131.084706
## iter 270 value 129.388314
## iter 280 value 127.586687
## iter 290 value 125.492254
## iter 300 value 123.634320
## iter 310 value 121.746325
## iter 320 value 119.770700
## iter 330 value 116.800554
## iter 340 value 114.280010
## iter 350 value 112.137141
## iter 360 value 110.864362
## iter 370 value 110.563737
## iter 380 value 110.452383
## iter 390 value 110.264549
## iter 400 value 109.943746
## iter 410 value 109.704432
## iter 420 value 109.357755
## iter 430 value 108.886792
## iter 440 value 108.303051
## iter 450 value 107.464079
## iter 460 value 106.686670
## iter 470 value 105.821288
## iter 480 value 104.881676
## iter 490 value 104.054904
## iter 500 value 102.918903
## final value 102.918903
## stopped after 500 iterations
## # weights: 241
## initial value 1372968.306651
## iter 10 value 1170.623995
## iter 20 value 735.632998
## iter 30 value 632.049007
## iter 40 value 544.407516
## iter 50 value 427.962919
## iter 60 value 342.964965
## iter 70 value 287.459051
## iter 80 value 260.112230
## iter 90 value 234.280996
## iter 100 value 206.523434
## iter 110 value 192.114085
## iter 120 value 179.062349
## iter 130 value 166.527489
## iter 140 value 153.064342
## iter 150 value 142.514745
## iter 160 value 134.331668
## iter 170 value 130.198101
## iter 180 value 125.191962
## iter 190 value 119.377881
## iter 200 value 114.618292
## iter 210 value 109.915254
## iter 220 value 103.605734
## iter 230 value 98.510239
## iter 240 value 94.561267
## iter 250 value 90.567594
## iter 260 value 87.981678
## iter 270 value 86.240400
## iter 280 value 84.617589
## iter 290 value 82.273894
## iter 300 value 80.424550
## iter 310 value 78.110941
## iter 320 value 76.476324
## iter 330 value 74.292248
## iter 340 value 72.280307
## iter 350 value 70.222535
## iter 360 value 68.658065
## iter 370 value 67.480433
## iter 380 value 66.035567
## iter 390 value 64.494497
## iter 400 value 63.446779
## iter 410 value 62.564942
## iter 420 value 62.008232
## iter 430 value 61.570939
## iter 440 value 61.002621
## iter 450 value 59.970512
## iter 460 value 59.154846
## iter 470 value 58.221127
## iter 480 value 57.589656
## iter 490 value 57.435851
## iter 500 value 57.343203
## final value 57.343203
## stopped after 500 iterations
## # weights: 25
## initial value 1430460.437131
## iter 10 value 6192.721907
## iter 20 value 5263.162093
## iter 30 value 5176.136277
## iter 40 value 5168.031669
## iter 50 value 5089.330370
## iter 60 value 4881.674891
## iter 70 value 4454.241373
## iter 80 value 2551.895501
## iter 90 value 1623.266511
## iter 100 value 1403.561932
## iter 110 value 1342.058141
## iter 120 value 1316.912993
## iter 130 value 1294.208753
## iter 140 value 1286.474221
## iter 150 value 1283.094032
## iter 160 value 1281.159469
## iter 170 value 1281.118938
## final value 1281.115241
## converged
## # weights: 61
## initial value 1396403.612522
## iter 10 value 156801.679997
## iter 20 value 9255.601861
## iter 30 value 5820.746535
## iter 40 value 4175.369343
## iter 50 value 2776.615704
## iter 60 value 2341.844432
## iter 70 value 2258.341534
## iter 80 value 2090.884091
## iter 90 value 1610.385517
## iter 100 value 1365.834262
## iter 110 value 1206.686385
## iter 120 value 1007.860471
## iter 130 value 860.712021
## iter 140 value 817.375927
## iter 150 value 780.292554
## iter 160 value 753.563689
## iter 170 value 736.667218
## iter 180 value 721.136633
## iter 190 value 705.856157
## iter 200 value 687.779513
## iter 210 value 676.467965
## iter 220 value 673.194834
## iter 230 value 660.822517
## iter 240 value 647.708209
## iter 250 value 637.743736
## iter 260 value 629.059467
## iter 270 value 625.919490
## iter 280 value 624.196927
## iter 290 value 622.561522
## iter 300 value 620.361731
## iter 310 value 618.560679
## iter 320 value 617.385723
## iter 330 value 616.722429
## iter 340 value 616.714215
## iter 350 value 616.652881
## iter 360 value 616.578315
## iter 370 value 615.665720
## iter 380 value 614.923055
## iter 390 value 614.485093
## iter 400 value 614.195769
## iter 410 value 614.126088
## iter 420 value 614.098984
## iter 430 value 614.085006
## iter 440 value 614.077636
## iter 450 value 614.074287
## final value 614.074150
## converged
## # weights: 121
## initial value 1388293.363406
## iter 10 value 1383.165057
## iter 20 value 885.247952
## iter 30 value 695.660265
## iter 40 value 592.360729
## iter 50 value 530.887238
## iter 60 value 479.486363
## iter 70 value 462.548999
## iter 80 value 451.181957
## iter 90 value 435.353896
## iter 100 value 410.620112
## iter 110 value 394.350099
## iter 120 value 385.153318
## iter 130 value 381.087108
## iter 140 value 378.335145
## iter 150 value 372.178297
## iter 160 value 364.028707
## iter 170 value 356.849707
## iter 180 value 348.375675
## iter 190 value 341.628982
## iter 200 value 338.214983
## iter 210 value 330.911257
## iter 220 value 317.532809
## iter 230 value 311.188692
## iter 240 value 301.231592
## iter 250 value 296.364708
## iter 260 value 294.794725
## iter 270 value 290.425789
## iter 280 value 285.353601
## iter 290 value 278.480588
## iter 300 value 272.256375
## iter 310 value 263.844181
## iter 320 value 259.584139
## iter 330 value 255.130565
## iter 340 value 249.075407
## iter 350 value 246.161343
## iter 360 value 242.799917
## iter 370 value 241.242062
## iter 380 value 240.429648
## iter 390 value 240.284215
## iter 400 value 240.150351
## iter 410 value 239.883590
## iter 420 value 239.669712
## iter 430 value 239.613921
## iter 440 value 239.522029
## iter 450 value 239.283229
## iter 460 value 238.991782
## iter 470 value 238.453257
## iter 480 value 237.994992
## iter 490 value 237.738315
## iter 500 value 237.651468
## final value 237.651468
## stopped after 500 iterations
## # weights: 181
## initial value 1359360.663325
## iter 10 value 1401.921210
## iter 20 value 875.177878
## iter 30 value 656.996690
## iter 40 value 518.820492
## iter 50 value 462.048776
## iter 60 value 405.161586
## iter 70 value 339.862593
## iter 80 value 301.241940
## iter 90 value 274.229864
## iter 100 value 254.383086
## iter 110 value 240.361910
## iter 120 value 226.174053
## iter 130 value 218.524045
## iter 140 value 211.111749
## iter 150 value 201.036837
## iter 160 value 189.585912
## iter 170 value 180.469228
## iter 180 value 170.126855
## iter 190 value 157.838030
## iter 200 value 149.298143
## iter 210 value 143.991844
## iter 220 value 140.057437
## iter 230 value 137.532127
## iter 240 value 136.269383
## iter 250 value 135.180173
## iter 260 value 134.072039
## iter 270 value 132.887361
## iter 280 value 130.873392
## iter 290 value 129.559743
## iter 300 value 128.438907
## iter 310 value 127.581425
## iter 320 value 127.098398
## iter 330 value 126.701337
## iter 340 value 126.482498
## iter 350 value 126.177734
## iter 360 value 125.963367
## iter 370 value 125.891295
## iter 380 value 125.857253
## iter 390 value 125.788713
## iter 400 value 125.710703
## iter 410 value 125.571493
## iter 420 value 125.439709
## iter 430 value 125.125151
## iter 440 value 124.694451
## iter 450 value 124.137828
## iter 460 value 123.722588
## iter 470 value 123.426111
## iter 480 value 123.152354
## iter 490 value 122.335544
## iter 500 value 121.385912
## final value 121.385912
## stopped after 500 iterations
## # weights: 241
## initial value 1339079.578028
## iter 10 value 2295.304643
## iter 20 value 942.311683
## iter 30 value 713.771595
## iter 40 value 566.067968
## iter 50 value 490.033781
## iter 60 value 430.538978
## iter 70 value 362.603381
## iter 80 value 319.972221
## iter 90 value 293.257829
## iter 100 value 270.366671
## iter 110 value 250.868414
## iter 120 value 232.745818
## iter 130 value 219.992035
## iter 140 value 211.611574
## iter 150 value 201.267775
## iter 160 value 192.145775
## iter 170 value 177.589026
## iter 180 value 166.659839
## iter 190 value 156.404584
## iter 200 value 150.844173
## iter 210 value 145.291709
## iter 220 value 139.904840
## iter 230 value 133.461468
## iter 240 value 128.691452
## iter 250 value 124.241918
## iter 260 value 120.144342
## iter 270 value 115.950384
## iter 280 value 112.089584
## iter 290 value 107.784777
## iter 300 value 103.943651
## iter 310 value 98.856854
## iter 320 value 94.423971
## iter 330 value 89.956071
## iter 340 value 85.707613
## iter 350 value 82.353643
## iter 360 value 79.497769
## iter 370 value 76.804721
## iter 380 value 74.571519
## iter 390 value 73.214677
## iter 400 value 71.836160
## iter 410 value 70.747803
## iter 420 value 69.745380
## iter 430 value 69.151290
## iter 440 value 68.726440
## iter 450 value 68.170816
## iter 460 value 67.481096
## iter 470 value 66.590129
## iter 480 value 65.743445
## iter 490 value 65.329004
## iter 500 value 65.273394
## final value 65.273394
## stopped after 500 iterations
## # weights: 25
## initial value 1371306.977207
## iter 10 value 6163.431950
## iter 20 value 5740.578549
## iter 30 value 5724.250739
## iter 40 value 5703.430395
## iter 50 value 5673.624462
## final value 5673.568283
## converged
## # weights: 61
## initial value 1356619.584693
## iter 10 value 2315.114235
## iter 20 value 1665.102298
## iter 30 value 1462.016843
## iter 40 value 1206.160009
## iter 50 value 1086.399977
## iter 60 value 982.460133
## iter 70 value 866.940522
## iter 80 value 773.655021
## iter 90 value 743.286615
## iter 100 value 725.367140
## iter 110 value 716.357390
## iter 120 value 701.828081
## iter 130 value 689.110762
## iter 140 value 684.861526
## iter 150 value 682.639225
## iter 160 value 679.578819
## iter 170 value 677.936305
## iter 180 value 675.877074
## iter 190 value 675.245495
## iter 200 value 675.151190
## iter 210 value 675.003065
## iter 220 value 674.894572
## iter 230 value 674.888210
## iter 240 value 674.878981
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## iter 260 value 674.813656
## iter 270 value 674.759781
## iter 280 value 674.552664
## iter 290 value 674.354239
## iter 300 value 674.115244
## iter 310 value 673.920097
## iter 320 value 673.882352
## iter 330 value 673.869954
## final value 673.829711
## converged
## # weights: 121
## initial value 1414504.383448
## iter 10 value 1277.136590
## iter 20 value 856.073759
## iter 30 value 696.579075
## iter 40 value 603.528958
## iter 50 value 527.646195
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## iter 70 value 418.231172
## iter 80 value 399.746059
## iter 90 value 388.031543
## iter 100 value 378.898658
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## iter 140 value 337.203507
## iter 150 value 327.520597
## iter 160 value 319.423250
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## iter 180 value 311.274687
## iter 190 value 307.789178
## iter 200 value 305.134440
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## iter 230 value 298.619777
## iter 240 value 296.963919
## iter 250 value 296.434565
## iter 260 value 296.220354
## iter 270 value 295.976852
## iter 280 value 295.180732
## iter 290 value 294.270332
## iter 300 value 293.856354
## iter 310 value 293.733665
## iter 320 value 293.170375
## iter 330 value 290.299040
## iter 340 value 288.018065
## iter 350 value 285.239615
## iter 360 value 280.105272
## iter 370 value 277.792216
## iter 380 value 271.443322
## iter 390 value 267.847396
## iter 400 value 263.243480
## iter 410 value 262.496018
## iter 420 value 261.605023
## iter 430 value 261.354269
## iter 440 value 261.286821
## iter 450 value 261.226228
## iter 460 value 261.127786
## iter 470 value 261.041762
## iter 480 value 261.004344
## iter 490 value 260.896863
## iter 500 value 260.877307
## final value 260.877307
## stopped after 500 iterations
## # weights: 181
## initial value 1386840.610073
## iter 10 value 1110.872717
## iter 20 value 772.085557
## iter 30 value 628.226471
## iter 40 value 460.964891
## iter 50 value 383.573460
## iter 60 value 341.633726
## iter 70 value 309.692626
## iter 80 value 263.910697
## iter 90 value 233.178115
## iter 100 value 219.739706
## iter 110 value 208.650735
## iter 120 value 196.330145
## iter 130 value 186.609681
## iter 140 value 174.323692
## iter 150 value 159.330326
## iter 160 value 148.764315
## iter 170 value 141.767555
## iter 180 value 134.696091
## iter 190 value 128.460008
## iter 200 value 122.750652
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## iter 220 value 115.432883
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## iter 240 value 111.177109
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## iter 270 value 106.878072
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## iter 300 value 103.097125
## iter 310 value 102.170722
## iter 320 value 101.641580
## iter 330 value 101.313430
## iter 340 value 100.576313
## iter 350 value 99.653474
## iter 360 value 99.002302
## iter 370 value 98.713766
## iter 380 value 98.596451
## iter 390 value 98.435520
## iter 400 value 98.137061
## iter 410 value 97.927819
## iter 420 value 97.356260
## iter 430 value 96.681623
## iter 440 value 96.220542
## iter 450 value 95.894381
## iter 460 value 95.586668
## iter 470 value 95.245946
## iter 480 value 94.586872
## iter 490 value 94.017195
## iter 500 value 93.008983
## final value 93.008983
## stopped after 500 iterations
## # weights: 241
## initial value 1399979.248215
## iter 10 value 3258.873150
## iter 20 value 947.290203
## iter 30 value 665.382210
## iter 40 value 473.081693
## iter 50 value 374.643924
## iter 60 value 318.499178
## iter 70 value 276.049849
## iter 80 value 236.073737
## iter 90 value 207.059490
## iter 100 value 173.152816
## iter 110 value 136.497582
## iter 120 value 118.893924
## iter 130 value 106.574788
## iter 140 value 96.096820
## iter 150 value 89.034156
## iter 160 value 81.835465
## iter 170 value 76.953343
## iter 180 value 70.811236
## iter 190 value 65.132480
## iter 200 value 60.455958
## iter 210 value 56.354792
## iter 220 value 52.486682
## iter 230 value 49.864610
## iter 240 value 47.988067
## iter 250 value 46.385572
## iter 260 value 43.862486
## iter 270 value 42.161818
## iter 280 value 40.899606
## iter 290 value 39.534512
## iter 300 value 38.297851
## iter 310 value 37.051015
## iter 320 value 35.446105
## iter 330 value 34.168357
## iter 340 value 33.033579
## iter 350 value 31.951122
## iter 360 value 30.829325
## iter 370 value 29.631059
## iter 380 value 28.717831
## iter 390 value 27.589415
## iter 400 value 26.797909
## iter 410 value 26.194780
## iter 420 value 25.574603
## iter 430 value 24.900634
## iter 440 value 24.466159
## iter 450 value 24.066124
## iter 460 value 23.693180
## iter 470 value 23.432414
## iter 480 value 23.227532
## iter 490 value 23.116645
## iter 500 value 23.082550
## final value 23.082550
## stopped after 500 iterations
## # weights: 25
## initial value 1396719.500964
## iter 10 value 14442.509209
## iter 20 value 8318.877569
## iter 30 value 2449.613836
## iter 40 value 1442.390049
## iter 50 value 1264.974398
## iter 60 value 1229.307221
## iter 70 value 1192.892924
## iter 80 value 1180.448678
## iter 90 value 1176.394756
## iter 100 value 1172.649592
## iter 110 value 1151.043775
## iter 120 value 1139.618268
## iter 130 value 1137.936695
## iter 140 value 1135.762766
## iter 150 value 1135.621121
## iter 160 value 1135.604907
## iter 170 value 1135.571068
## iter 180 value 1135.545407
## final value 1135.531521
## converged
## # weights: 61
## initial value 1395559.158788
## iter 10 value 2965.362259
## iter 20 value 1648.111629
## iter 30 value 1078.679235
## iter 40 value 858.968683
## iter 50 value 763.990663
## iter 60 value 728.855353
## iter 70 value 688.317954
## iter 80 value 659.053913
## iter 90 value 640.301413
## iter 100 value 622.044528
## iter 110 value 610.951452
## iter 120 value 600.766610
## iter 130 value 595.506993
## iter 140 value 593.075413
## iter 150 value 588.107703
## iter 160 value 579.301858
## iter 170 value 571.500934
## iter 180 value 565.002137
## iter 190 value 563.150493
## iter 200 value 561.290695
## iter 210 value 560.476340
## iter 220 value 560.281806
## iter 230 value 560.173274
## iter 240 value 560.024207
## iter 250 value 559.949920
## iter 260 value 559.567576
## iter 270 value 558.756010
## iter 280 value 558.186569
## iter 290 value 556.313216
## iter 300 value 555.890766
## iter 310 value 555.799140
## iter 320 value 555.623466
## iter 330 value 555.556972
## iter 340 value 555.511387
## iter 350 value 555.476564
## iter 360 value 555.468100
## iter 360 value 555.468096
## iter 360 value 555.468093
## final value 555.468093
## converged
## # weights: 121
## initial value 1367573.323344
## iter 10 value 1387.922761
## iter 20 value 902.438461
## iter 30 value 741.281949
## iter 40 value 620.089931
## iter 50 value 553.786006
## iter 60 value 507.241444
## iter 70 value 462.427228
## iter 80 value 433.559924
## iter 90 value 413.686746
## iter 100 value 395.764417
## iter 110 value 381.836097
## iter 120 value 374.803124
## iter 130 value 366.707894
## iter 140 value 362.038174
## iter 150 value 355.314352
## iter 160 value 349.264977
## iter 170 value 343.676292
## iter 180 value 337.771236
## iter 190 value 333.605960
## iter 200 value 329.821507
## iter 210 value 324.452005
## iter 220 value 320.154650
## iter 230 value 317.146476
## iter 240 value 315.115750
## iter 250 value 314.159772
## iter 260 value 313.338361
## iter 270 value 312.229241
## iter 280 value 309.663396
## iter 290 value 306.046459
## iter 300 value 302.279587
## iter 310 value 298.687355
## iter 320 value 294.957058
## iter 330 value 291.051472
## iter 340 value 289.002688
## iter 350 value 285.647698
## iter 360 value 282.719438
## iter 370 value 280.046920
## iter 380 value 277.528513
## iter 390 value 274.400966
## iter 400 value 271.914406
## iter 410 value 269.263666
## iter 420 value 266.516046
## iter 430 value 265.142300
## iter 440 value 263.885987
## iter 450 value 262.816387
## iter 460 value 261.020055
## iter 470 value 258.699086
## iter 480 value 255.618241
## iter 490 value 253.725632
## iter 500 value 253.034260
## final value 253.034260
## stopped after 500 iterations
## # weights: 181
## initial value 1376459.602667
## iter 10 value 1127.345852
## iter 20 value 832.945479
## iter 30 value 674.005026
## iter 40 value 583.374431
## iter 50 value 505.273381
## iter 60 value 469.719277
## iter 70 value 435.532127
## iter 80 value 389.899075
## iter 90 value 352.911699
## iter 100 value 323.992758
## iter 110 value 298.214756
## iter 120 value 274.799992
## iter 130 value 260.649590
## iter 140 value 248.773487
## iter 150 value 230.974150
## iter 160 value 213.545800
## iter 170 value 204.372007
## iter 180 value 198.059819
## iter 190 value 192.314134
## iter 200 value 185.412410
## iter 210 value 177.952262
## iter 220 value 170.755753
## iter 230 value 166.903620
## iter 240 value 163.051089
## iter 250 value 160.017337
## iter 260 value 157.171586
## iter 270 value 154.385850
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## iter 290 value 149.698070
## iter 300 value 147.543353
## iter 310 value 143.980594
## iter 320 value 141.781824
## iter 330 value 139.406858
## iter 340 value 136.832988
## iter 350 value 135.208600
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## iter 380 value 133.280880
## iter 390 value 132.588687
## iter 400 value 131.662410
## iter 410 value 130.859474
## iter 420 value 129.970304
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## iter 440 value 127.352780
## iter 450 value 126.228489
## iter 460 value 123.407267
## iter 470 value 120.238405
## iter 480 value 118.793030
## iter 490 value 117.143551
## iter 500 value 113.849053
## final value 113.849053
## stopped after 500 iterations
## # weights: 241
## initial value 1389790.278838
## iter 10 value 1263.565629
## iter 20 value 793.948900
## iter 30 value 632.273659
## iter 40 value 520.049714
## iter 50 value 407.226424
## iter 60 value 347.868354
## iter 70 value 310.508556
## iter 80 value 279.572911
## iter 90 value 246.255188
## iter 100 value 212.968424
## iter 110 value 187.778570
## iter 120 value 169.578705
## iter 130 value 159.193569
## iter 140 value 151.159279
## iter 150 value 142.326458
## iter 160 value 134.003837
## iter 170 value 123.380027
## iter 180 value 116.739004
## iter 190 value 109.334043
## iter 200 value 104.365390
## iter 210 value 100.136316
## iter 220 value 97.064740
## iter 230 value 93.927804
## iter 240 value 88.875125
## iter 250 value 86.337503
## iter 260 value 83.020601
## iter 270 value 80.476937
## iter 280 value 78.064533
## iter 290 value 76.258054
## iter 300 value 74.680371
## iter 310 value 73.122328
## iter 320 value 71.469310
## iter 330 value 69.455467
## iter 340 value 67.549627
## iter 350 value 65.934425
## iter 360 value 64.218492
## iter 370 value 62.305616
## iter 380 value 60.605498
## iter 390 value 59.204247
## iter 400 value 57.526085
## iter 410 value 55.349930
## iter 420 value 53.616819
## iter 430 value 52.351348
## iter 440 value 51.393803
## iter 450 value 50.241243
## iter 460 value 48.883759
## iter 470 value 47.611422
## iter 480 value 46.592423
## iter 490 value 46.342161
## iter 500 value 46.248030
## final value 46.248030
## stopped after 500 iterations
## # weights: 25
## initial value 1386822.515707
## iter 10 value 15524.497983
## iter 20 value 13787.875760
## iter 30 value 5598.753686
## iter 40 value 4652.935684
## iter 50 value 3910.788261
## iter 60 value 2928.643200
## iter 70 value 2458.895845
## iter 80 value 1769.742705
## iter 90 value 1546.110268
## iter 100 value 1416.175404
## iter 110 value 1301.132530
## iter 120 value 1258.077407
## iter 130 value 1206.200747
## iter 140 value 1157.611149
## iter 150 value 1135.895858
## iter 160 value 1133.170304
## iter 170 value 1132.384037
## iter 180 value 1131.459750
## final value 1131.435314
## converged
## # weights: 61
## initial value 1364693.216837
## iter 10 value 14839.183375
## iter 20 value 2275.262623
## iter 30 value 1670.628631
## iter 40 value 1321.568565
## iter 50 value 1154.518087
## iter 60 value 1087.910583
## iter 70 value 1018.805567
## iter 80 value 989.827995
## iter 90 value 960.107556
## iter 100 value 928.462403
## iter 110 value 888.292781
## iter 120 value 859.054365
## iter 130 value 846.846802
## iter 140 value 838.287877
## iter 150 value 816.122115
## iter 160 value 792.460452
## iter 170 value 785.912732
## iter 180 value 782.657998
## iter 190 value 780.667595
## iter 200 value 779.275156
## iter 210 value 779.111645
## iter 220 value 779.036366
## iter 230 value 778.989731
## iter 240 value 778.049105
## iter 250 value 772.677945
## iter 260 value 765.026100
## iter 270 value 760.684713
## iter 280 value 760.062316
## iter 290 value 760.053246
## final value 760.053201
## converged
## # weights: 121
## initial value 1380308.695246
## iter 10 value 1941.494081
## iter 20 value 1195.631407
## iter 30 value 1019.595749
## iter 40 value 885.958336
## iter 50 value 815.992857
## iter 60 value 763.370679
## iter 70 value 732.576310
## iter 80 value 711.444962
## iter 90 value 696.220971
## iter 100 value 677.345606
## iter 110 value 664.857428
## iter 120 value 650.971994
## iter 130 value 639.772116
## iter 140 value 628.478801
## iter 150 value 618.955114
## iter 160 value 610.799521
## iter 170 value 606.893924
## iter 180 value 601.448907
## iter 190 value 594.374696
## iter 200 value 589.413595
## iter 210 value 581.873617
## iter 220 value 576.469197
## iter 230 value 572.721473
## iter 240 value 570.470259
## iter 250 value 569.720771
## iter 260 value 568.911611
## iter 270 value 567.781883
## iter 280 value 566.993064
## iter 290 value 566.179292
## iter 300 value 565.794904
## iter 310 value 565.718524
## iter 320 value 565.697809
## iter 330 value 565.695020
## iter 340 value 565.694307
## iter 340 value 565.694306
## iter 340 value 565.694306
## final value 565.694306
## converged
## # weights: 181
## initial value 1374836.899352
## iter 10 value 1128.775356
## iter 20 value 844.420908
## iter 30 value 732.954771
## iter 40 value 640.927126
## iter 50 value 587.094388
## iter 60 value 559.877043
## iter 70 value 532.379611
## iter 80 value 510.252722
## iter 90 value 489.555234
## iter 100 value 476.207583
## iter 110 value 463.287706
## iter 120 value 453.160651
## iter 130 value 446.503101
## iter 140 value 440.052510
## iter 150 value 433.779761
## iter 160 value 425.622641
## iter 170 value 414.585996
## iter 180 value 404.975692
## iter 190 value 400.704902
## iter 200 value 398.435012
## iter 210 value 395.758904
## iter 220 value 391.175072
## iter 230 value 386.413329
## iter 240 value 384.734740
## iter 250 value 383.020356
## iter 260 value 380.883195
## iter 270 value 379.378749
## iter 280 value 378.379941
## iter 290 value 377.498714
## iter 300 value 377.017491
## iter 310 value 376.273884
## iter 320 value 374.346056
## iter 330 value 372.433115
## iter 340 value 371.716468
## iter 350 value 371.206302
## iter 360 value 370.970846
## iter 370 value 370.828252
## iter 380 value 370.602865
## iter 390 value 370.280140
## iter 400 value 369.949448
## iter 410 value 369.729400
## iter 420 value 369.386510
## iter 430 value 368.884259
## iter 440 value 368.172760
## iter 450 value 367.425937
## iter 460 value 367.042389
## iter 470 value 366.858327
## iter 480 value 366.302104
## iter 490 value 365.517845
## iter 500 value 365.259951
## final value 365.259951
## stopped after 500 iterations
## # weights: 241
## initial value 1433429.552750
## iter 10 value 1529.795428
## iter 20 value 965.684454
## iter 30 value 790.941457
## iter 40 value 653.460221
## iter 50 value 585.006296
## iter 60 value 547.056305
## iter 70 value 521.948313
## iter 80 value 501.495627
## iter 90 value 484.720068
## iter 100 value 469.993613
## iter 110 value 459.783308
## iter 120 value 452.637348
## iter 130 value 445.785425
## iter 140 value 436.124961
## iter 150 value 427.514534
## iter 160 value 420.568096
## iter 170 value 413.966086
## iter 180 value 408.618559
## iter 190 value 403.791467
## iter 200 value 400.631191
## iter 210 value 398.252841
## iter 220 value 396.179856
## iter 230 value 393.413376
## iter 240 value 390.967789
## iter 250 value 388.476492
## iter 260 value 386.191829
## iter 270 value 382.198473
## iter 280 value 377.512272
## iter 290 value 373.127737
## iter 300 value 369.913187
## iter 310 value 366.542688
## iter 320 value 363.922714
## iter 330 value 361.745430
## iter 340 value 360.224391
## iter 350 value 358.675647
## iter 360 value 357.330972
## iter 370 value 356.312781
## iter 380 value 354.963312
## iter 390 value 353.658001
## iter 400 value 352.003418
## iter 410 value 350.450055
## iter 420 value 349.111709
## iter 430 value 348.219124
## iter 440 value 347.520189
## iter 450 value 346.329727
## iter 460 value 345.171299
## iter 470 value 344.155835
## iter 480 value 343.317025
## iter 490 value 342.881579
## iter 500 value 342.563643
## final value 342.563643
## stopped after 500 iterations
## # weights: 25
## initial value 1386845.223632
## iter 10 value 6249.655546
## iter 20 value 5381.032915
## iter 30 value 5365.504934
## iter 40 value 5360.341599
## iter 50 value 5316.480930
## iter 60 value 5200.182221
## iter 70 value 4867.016027
## iter 80 value 4245.792816
## iter 90 value 3908.976463
## iter 100 value 3576.700343
## iter 110 value 2962.377398
## iter 120 value 1966.052646
## iter 130 value 1489.568052
## iter 140 value 1394.010661
## iter 150 value 1371.074144
## iter 160 value 1333.086060
## iter 170 value 1276.286310
## iter 180 value 1259.642071
## iter 190 value 1252.414447
## iter 200 value 1249.596952
## iter 210 value 1247.815759
## iter 220 value 1246.132893
## iter 230 value 1245.142563
## iter 240 value 1244.923777
## final value 1244.923341
## converged
## # weights: 61
## initial value 1398990.279805
## iter 10 value 300265.287882
## iter 20 value 17594.371402
## iter 30 value 10314.928058
## iter 40 value 6158.556648
## iter 50 value 4059.392208
## iter 60 value 2370.793201
## iter 70 value 1318.551290
## iter 80 value 1057.868321
## iter 90 value 960.888073
## iter 100 value 903.440898
## iter 110 value 853.594511
## iter 120 value 836.504709
## iter 130 value 828.472208
## iter 140 value 822.273228
## iter 150 value 815.422686
## iter 160 value 796.430564
## iter 170 value 777.923118
## iter 180 value 758.187537
## iter 190 value 739.239602
## iter 200 value 726.693376
## iter 210 value 715.405384
## iter 220 value 712.459048
## iter 230 value 706.752590
## iter 240 value 695.826603
## iter 250 value 693.557235
## iter 260 value 693.403977
## iter 270 value 692.376719
## iter 280 value 689.528007
## iter 290 value 682.595336
## iter 300 value 669.161832
## iter 310 value 666.248076
## iter 320 value 664.874063
## iter 330 value 664.814038
## iter 340 value 664.347113
## iter 350 value 662.075452
## iter 360 value 659.267801
## iter 370 value 658.584497
## iter 380 value 657.937085
## iter 390 value 657.606759
## iter 400 value 656.442250
## iter 410 value 656.214031
## iter 420 value 653.285933
## iter 430 value 647.437524
## iter 440 value 645.618081
## iter 450 value 645.332349
## iter 460 value 645.314107
## iter 470 value 645.048570
## iter 480 value 644.601991
## iter 490 value 642.344036
## iter 500 value 642.079762
## final value 642.079762
## stopped after 500 iterations
## # weights: 121
## initial value 1294845.404213
## iter 10 value 3144.129144
## iter 20 value 1372.187645
## iter 30 value 887.961238
## iter 40 value 677.177523
## iter 50 value 589.167057
## iter 60 value 540.391271
## iter 70 value 491.583665
## iter 80 value 462.226517
## iter 90 value 440.941424
## iter 100 value 425.586193
## iter 110 value 412.021702
## iter 120 value 400.313508
## iter 130 value 389.142971
## iter 140 value 375.025807
## iter 150 value 370.189359
## iter 160 value 365.803205
## iter 170 value 360.776090
## iter 180 value 357.029782
## iter 190 value 354.750764
## iter 200 value 352.244282
## iter 210 value 349.081853
## iter 220 value 343.006646
## iter 230 value 338.665130
## iter 240 value 335.571192
## iter 250 value 334.635232
## iter 260 value 334.054403
## iter 270 value 332.697662
## iter 280 value 329.347168
## iter 290 value 323.855979
## iter 300 value 320.509499
## iter 310 value 317.778994
## iter 320 value 316.307775
## iter 330 value 313.449930
## iter 340 value 306.301199
## iter 350 value 304.751253
## iter 360 value 303.485817
## iter 370 value 303.160653
## iter 380 value 303.035276
## iter 390 value 302.992172
## iter 400 value 302.781505
## iter 410 value 302.345765
## iter 420 value 300.224342
## iter 430 value 297.837711
## iter 440 value 296.708962
## iter 450 value 296.066162
## iter 460 value 295.619175
## iter 470 value 295.201915
## iter 480 value 295.101066
## iter 490 value 295.034560
## iter 500 value 294.984937
## final value 294.984937
## stopped after 500 iterations
## # weights: 181
## initial value 1398210.132030
## iter 10 value 1212.487486
## iter 20 value 790.755675
## iter 30 value 599.559610
## iter 40 value 461.524122
## iter 50 value 376.471882
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## iter 500 value 118.237661
## final value 118.237661
## stopped after 500 iterations
## # weights: 241
## initial value 1459404.078220
## iter 10 value 1487.519872
## iter 20 value 857.221851
## iter 30 value 644.349686
## iter 40 value 466.824808
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## iter 480 value 67.682656
## iter 490 value 67.453687
## iter 500 value 67.382174
## final value 67.382174
## stopped after 500 iterations
## # weights: 25
## initial value 1348348.565099
## iter 10 value 16499.206246
## iter 20 value 16499.163274
## iter 20 value 16499.163123
## iter 20 value 16499.163094
## final value 16499.163094
## converged
## # weights: 61
## initial value 1396640.513174
## iter 10 value 4718.844536
## iter 20 value 3443.463331
## iter 30 value 2533.873000
## iter 40 value 2086.752691
## iter 50 value 1607.773781
## iter 60 value 1101.315221
## iter 70 value 1053.582764
## iter 80 value 1018.449464
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## iter 100 value 891.287658
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## iter 470 value 660.951895
## iter 480 value 652.179496
## iter 490 value 648.466928
## iter 500 value 637.004503
## final value 637.004503
## stopped after 500 iterations
## # weights: 121
## initial value 1348256.353507
## iter 10 value 1481.244996
## iter 20 value 889.570108
## iter 30 value 723.199006
## iter 40 value 642.364368
## iter 50 value 584.535771
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## iter 200 value 332.202344
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## iter 470 value 277.096578
## iter 480 value 276.908288
## iter 490 value 276.720911
## iter 500 value 276.705110
## final value 276.705110
## stopped after 500 iterations
## # weights: 181
## initial value 1445155.038464
## iter 10 value 1610.565559
## iter 20 value 861.819704
## iter 30 value 609.107140
## iter 40 value 522.382345
## iter 50 value 430.293145
## iter 60 value 338.884494
## iter 70 value 308.797613
## iter 80 value 285.552644
## iter 90 value 271.115860
## iter 100 value 261.662764
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## iter 500 value 150.331236
## final value 150.331236
## stopped after 500 iterations
## # weights: 241
## initial value 1409936.924068
## iter 10 value 1073.645391
## iter 20 value 798.573291
## iter 30 value 673.105183
## iter 40 value 529.072443
## iter 50 value 435.605381
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## iter 90 value 255.045402
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## iter 400 value 40.602270
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## iter 420 value 40.065167
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## iter 480 value 38.741348
## iter 490 value 38.654162
## iter 500 value 38.622297
## final value 38.622297
## stopped after 500 iterations
## # weights: 25
## initial value 1373823.380082
## iter 10 value 6253.279474
## iter 20 value 5725.046452
## iter 30 value 5578.631642
## iter 40 value 5492.007544
## iter 50 value 5490.391503
## iter 60 value 5488.023964
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## iter 80 value 5396.049880
## iter 90 value 5314.708813
## iter 100 value 5304.316394
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## iter 150 value 3051.899041
## iter 160 value 1695.725783
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## iter 180 value 1333.434226
## iter 190 value 1322.665567
## iter 200 value 1293.129959
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## iter 220 value 1280.491969
## iter 230 value 1278.063909
## iter 240 value 1277.371212
## iter 250 value 1277.366433
## final value 1277.366333
## converged
## # weights: 61
## initial value 1364398.873508
## iter 10 value 11905.873694
## iter 20 value 2667.673914
## iter 30 value 2344.956231
## iter 40 value 2182.427176
## iter 50 value 1877.229347
## iter 60 value 1543.205346
## iter 70 value 1329.184053
## iter 80 value 1207.074647
## iter 90 value 1013.733243
## iter 100 value 839.146596
## iter 110 value 801.211787
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## iter 240 value 768.420010
## iter 250 value 767.649977
## iter 260 value 767.246021
## iter 270 value 767.157348
## iter 280 value 767.093022
## iter 290 value 767.062501
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## iter 320 value 766.981909
## iter 330 value 766.816382
## iter 340 value 766.745742
## iter 350 value 766.688524
## iter 360 value 766.675249
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## iter 390 value 766.642445
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## iter 420 value 766.353365
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## iter 470 value 754.778566
## iter 480 value 754.637851
## iter 490 value 754.514668
## iter 500 value 754.444447
## final value 754.444447
## stopped after 500 iterations
## # weights: 121
## initial value 1356508.019062
## iter 10 value 1253.270288
## iter 20 value 913.879343
## iter 30 value 736.663029
## iter 40 value 625.605150
## iter 50 value 570.936527
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## iter 300 value 273.827177
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## iter 460 value 248.237593
## iter 470 value 248.035217
## iter 480 value 247.998048
## iter 490 value 247.987791
## iter 500 value 247.987450
## final value 247.987450
## stopped after 500 iterations
## # weights: 181
## initial value 1336740.011918
## iter 10 value 1181.090906
## iter 20 value 759.032193
## iter 30 value 605.281300
## iter 40 value 496.083022
## iter 50 value 439.235673
## iter 60 value 376.746852
## iter 70 value 326.064156
## iter 80 value 286.730812
## iter 90 value 253.279918
## iter 100 value 235.067294
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## iter 460 value 83.261688
## iter 470 value 82.608659
## iter 480 value 81.882700
## iter 490 value 81.511698
## iter 500 value 81.218299
## final value 81.218299
## stopped after 500 iterations
## # weights: 241
## initial value 1399188.347897
## iter 10 value 1409.625308
## iter 20 value 795.392802
## iter 30 value 662.767686
## iter 40 value 547.238388
## iter 50 value 434.876154
## iter 60 value 345.910558
## iter 70 value 298.936100
## iter 80 value 262.613598
## iter 90 value 230.207643
## iter 100 value 206.227783
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## iter 260 value 90.223053
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## iter 280 value 78.570581
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## iter 310 value 64.352941
## iter 320 value 59.498035
## iter 330 value 55.835749
## iter 340 value 53.390554
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## iter 360 value 49.077468
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## iter 390 value 46.430641
## iter 400 value 45.792278
## iter 410 value 45.137261
## iter 420 value 44.236903
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## iter 450 value 42.941393
## iter 460 value 42.636025
## iter 470 value 42.363021
## iter 480 value 42.018238
## iter 490 value 41.560420
## iter 500 value 41.417481
## final value 41.417481
## stopped after 500 iterations
## # weights: 25
## initial value 1403786.083865
## iter 10 value 109090.575854
## iter 20 value 12235.275131
## iter 30 value 10458.293873
## iter 40 value 7729.915632
## iter 50 value 7098.728154
## iter 60 value 6936.036218
## iter 70 value 6845.601719
## iter 80 value 6831.785691
## iter 90 value 5306.701518
## iter 100 value 5273.013103
## iter 110 value 5161.193596
## iter 120 value 5115.887293
## iter 130 value 5036.311116
## iter 140 value 4924.146262
## iter 150 value 4875.879097
## iter 160 value 4874.923199
## iter 170 value 4874.905242
## iter 180 value 4874.893180
## iter 190 value 4874.847029
## iter 200 value 4874.809686
## iter 210 value 4874.799255
## iter 220 value 4851.453366
## iter 230 value 4849.099113
## iter 240 value 4846.060725
## iter 250 value 4843.024676
## iter 260 value 4842.887685
## final value 4842.871187
## converged
## # weights: 61
## initial value 1393088.021461
## iter 10 value 3260.795846
## iter 20 value 1384.820038
## iter 30 value 1155.267180
## iter 40 value 994.360390
## iter 50 value 869.922356
## iter 60 value 785.870009
## iter 70 value 731.120828
## iter 80 value 678.912984
## iter 90 value 659.770267
## iter 100 value 649.324934
## iter 110 value 643.098544
## iter 120 value 639.799592
## iter 130 value 639.135584
## iter 140 value 638.384518
## iter 150 value 636.736730
## iter 160 value 633.972176
## iter 170 value 628.206410
## iter 180 value 616.282653
## iter 190 value 595.848528
## iter 200 value 569.711816
## iter 210 value 553.979811
## iter 220 value 545.787447
## iter 230 value 534.945899
## iter 240 value 527.557535
## iter 250 value 525.554159
## iter 260 value 525.277297
## iter 270 value 523.367951
## iter 280 value 521.871232
## iter 290 value 520.658230
## iter 300 value 520.458818
## iter 310 value 518.787633
## iter 320 value 512.649611
## iter 330 value 512.040457
## iter 340 value 511.492133
## iter 350 value 509.990577
## iter 360 value 508.364601
## iter 370 value 508.250028
## iter 380 value 508.247649
## iter 390 value 508.231024
## iter 400 value 508.211727
## iter 410 value 508.064736
## iter 420 value 507.918188
## iter 430 value 507.804248
## iter 440 value 507.720711
## iter 450 value 507.574598
## iter 460 value 507.561893
## iter 470 value 507.561175
## iter 480 value 507.558771
## iter 490 value 507.550793
## final value 507.550416
## converged
## # weights: 121
## initial value 1412465.598450
## iter 10 value 3644.689711
## iter 20 value 1284.277922
## iter 30 value 927.686067
## iter 40 value 858.829070
## iter 50 value 826.437010
## iter 60 value 792.697973
## iter 70 value 734.664321
## iter 80 value 705.302068
## iter 90 value 669.431413
## iter 100 value 615.021180
## iter 110 value 585.162706
## iter 120 value 561.139588
## iter 130 value 531.179863
## iter 140 value 514.026613
## iter 150 value 504.161953
## iter 160 value 490.860952
## iter 170 value 482.631429
## iter 180 value 481.610414
## iter 190 value 479.876922
## iter 200 value 477.322793
## iter 210 value 476.408946
## iter 220 value 476.174850
## iter 230 value 475.395736
## iter 240 value 474.708554
## iter 250 value 473.786295
## iter 260 value 473.523383
## iter 270 value 473.304851
## iter 280 value 472.774154
## iter 290 value 472.398132
## iter 300 value 471.855370
## iter 310 value 470.562349
## iter 320 value 469.715299
## iter 330 value 469.553279
## iter 340 value 469.370841
## iter 350 value 468.942093
## iter 360 value 468.300864
## iter 370 value 468.043216
## iter 380 value 467.725542
## iter 390 value 467.626573
## iter 400 value 467.561666
## iter 410 value 467.513369
## iter 420 value 467.451402
## iter 430 value 467.431944
## iter 440 value 467.405126
## iter 450 value 467.400114
## iter 460 value 467.398315
## final value 467.397823
## converged
## # weights: 181
## initial value 1378212.034278
## iter 10 value 1703.541153
## iter 20 value 1101.212112
## iter 30 value 731.917070
## iter 40 value 568.798338
## iter 50 value 462.899902
## iter 60 value 398.317097
## iter 70 value 360.462422
## iter 80 value 327.909791
## iter 90 value 304.697749
## iter 100 value 282.805433
## iter 110 value 262.578836
## iter 120 value 245.889765
## iter 130 value 229.466097
## iter 140 value 215.270058
## iter 150 value 207.713023
## iter 160 value 200.510845
## iter 170 value 195.583108
## iter 180 value 192.284509
## iter 190 value 188.651419
## iter 200 value 185.605565
## iter 210 value 183.961976
## iter 220 value 182.321087
## iter 230 value 181.391753
## iter 240 value 180.220902
## iter 250 value 178.327857
## iter 260 value 176.647529
## iter 270 value 173.243753
## iter 280 value 170.589587
## iter 290 value 168.088739
## iter 300 value 165.664329
## iter 310 value 164.400616
## iter 320 value 163.630390
## iter 330 value 162.671111
## iter 340 value 161.375750
## iter 350 value 159.708574
## iter 360 value 158.172092
## iter 370 value 157.059211
## iter 380 value 156.773046
## iter 390 value 156.177545
## iter 400 value 154.012173
## iter 410 value 150.051945
## iter 420 value 143.356092
## iter 430 value 139.358932
## iter 440 value 137.001456
## iter 450 value 135.145501
## iter 460 value 133.658801
## iter 470 value 132.715739
## iter 480 value 131.910983
## iter 490 value 131.344263
## iter 500 value 130.791752
## final value 130.791752
## stopped after 500 iterations
## # weights: 241
## initial value 1414928.952290
## iter 10 value 1112.083537
## iter 20 value 820.314474
## iter 30 value 627.938497
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## iter 50 value 352.983889
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## iter 270 value 38.989487
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## iter 320 value 31.052977
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## iter 340 value 29.467565
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## iter 360 value 27.523931
## iter 370 value 26.181882
## iter 380 value 25.403279
## iter 390 value 24.759019
## iter 400 value 24.351240
## iter 410 value 24.029185
## iter 420 value 23.818087
## iter 430 value 23.649284
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## iter 450 value 23.266357
## iter 460 value 22.540332
## iter 470 value 22.050322
## iter 480 value 21.749350
## iter 490 value 21.343178
## iter 500 value 21.085269
## final value 21.085269
## stopped after 500 iterations
## # weights: 25
## initial value 1403838.960057
## iter 10 value 506824.749182
## iter 20 value 44386.175785
## iter 30 value 15890.689843
## iter 40 value 9416.351899
## iter 50 value 8490.905770
## iter 60 value 5735.553237
## iter 70 value 5010.229939
## iter 80 value 4047.326041
## iter 90 value 3766.492297
## iter 100 value 2522.448204
## iter 110 value 1765.843199
## iter 120 value 1333.882634
## iter 130 value 1237.414213
## iter 140 value 1229.070896
## iter 150 value 1200.979018
## iter 160 value 1175.562164
## iter 170 value 1171.947238
## iter 180 value 1171.382560
## iter 190 value 1171.379816
## iter 190 value 1171.379813
## iter 200 value 1171.378623
## final value 1171.378169
## converged
## # weights: 61
## initial value 1355729.880341
## iter 10 value 10900.552477
## iter 20 value 6255.930602
## iter 30 value 4978.361437
## iter 40 value 4354.622460
## iter 50 value 3347.467992
## iter 60 value 2270.692950
## iter 70 value 1939.407852
## iter 80 value 1809.152073
## iter 90 value 1627.059129
## iter 100 value 1495.984986
## iter 110 value 1459.819661
## iter 120 value 1435.237228
## iter 130 value 1424.999084
## iter 140 value 1396.703192
## iter 150 value 1245.071424
## iter 160 value 1164.136867
## iter 170 value 1120.667877
## iter 180 value 1077.657534
## iter 190 value 1062.230791
## iter 200 value 1050.255460
## iter 210 value 1024.342159
## iter 220 value 1010.332015
## iter 230 value 988.807795
## iter 240 value 966.806886
## iter 250 value 945.875646
## iter 260 value 941.171965
## iter 270 value 936.601313
## iter 280 value 928.771873
## iter 290 value 924.891796
## iter 300 value 920.281473
## iter 310 value 917.349135
## iter 320 value 917.024758
## iter 330 value 916.934474
## iter 340 value 916.928242
## final value 916.928060
## converged
## # weights: 121
## initial value 1403073.843998
## iter 10 value 2107.523782
## iter 20 value 1362.300897
## iter 30 value 1185.796867
## iter 40 value 1048.747753
## iter 50 value 931.796074
## iter 60 value 855.519058
## iter 70 value 808.389906
## iter 80 value 782.364516
## iter 90 value 767.325278
## iter 100 value 755.388188
## iter 110 value 742.132719
## iter 120 value 726.371402
## iter 130 value 712.753263
## iter 140 value 683.452858
## iter 150 value 665.893149
## iter 160 value 650.454591
## iter 170 value 633.277032
## iter 180 value 612.447425
## iter 190 value 606.462573
## iter 200 value 600.277575
## iter 210 value 595.455302
## iter 220 value 592.182436
## iter 230 value 589.140085
## iter 240 value 586.357115
## iter 250 value 585.349842
## iter 260 value 584.342387
## iter 270 value 581.812184
## iter 280 value 580.365301
## iter 290 value 579.592517
## iter 300 value 579.106209
## iter 310 value 578.146926
## iter 320 value 577.544223
## iter 330 value 576.387130
## iter 340 value 575.632030
## iter 350 value 575.360008
## iter 360 value 575.273223
## iter 370 value 575.252466
## iter 380 value 575.246704
## final value 575.246490
## converged
## # weights: 181
## initial value 1392525.760123
## iter 10 value 1335.243737
## iter 20 value 937.276248
## iter 30 value 741.748519
## iter 40 value 637.818077
## iter 50 value 567.992999
## iter 60 value 517.702860
## iter 70 value 483.021601
## iter 80 value 464.102084
## iter 90 value 446.484026
## iter 100 value 430.887759
## iter 110 value 418.062586
## iter 120 value 408.244518
## iter 130 value 402.325369
## iter 140 value 397.906754
## iter 150 value 394.107682
## iter 160 value 386.600080
## iter 170 value 380.389622
## iter 180 value 375.817339
## iter 190 value 372.784996
## iter 200 value 370.527116
## iter 210 value 368.812003
## iter 220 value 367.364170
## iter 230 value 366.109081
## iter 240 value 362.394829
## iter 250 value 360.921792
## iter 260 value 360.240145
## iter 270 value 359.624404
## iter 280 value 359.210443
## iter 290 value 358.341089
## iter 300 value 355.190865
## iter 310 value 352.535360
## iter 320 value 351.164719
## iter 330 value 349.681241
## iter 340 value 348.278906
## iter 350 value 347.553564
## iter 360 value 346.485252
## iter 370 value 345.802900
## iter 380 value 345.348713
## iter 390 value 344.545281
## iter 400 value 343.145703
## iter 410 value 342.028837
## iter 420 value 340.858727
## iter 430 value 339.902367
## iter 440 value 339.284692
## iter 450 value 338.800417
## iter 460 value 338.495463
## iter 470 value 338.284241
## iter 480 value 338.161969
## iter 490 value 338.108534
## iter 500 value 338.072148
## final value 338.072148
## stopped after 500 iterations
## # weights: 241
## initial value 1389319.093287
## iter 10 value 1355.486815
## iter 20 value 1000.114558
## iter 30 value 805.397807
## iter 40 value 709.435649
## iter 50 value 630.817504
## iter 60 value 577.716356
## iter 70 value 548.936664
## iter 80 value 527.646623
## iter 90 value 501.442998
## iter 100 value 468.056672
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## iter 120 value 430.342575
## iter 130 value 417.665335
## iter 140 value 406.531938
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## iter 160 value 391.721459
## iter 170 value 387.764683
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## iter 190 value 377.288687
## iter 200 value 371.808477
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## iter 220 value 362.981632
## iter 230 value 359.147544
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## iter 260 value 345.669764
## iter 270 value 341.404619
## iter 280 value 338.552120
## iter 290 value 336.702187
## iter 300 value 335.357523
## iter 310 value 333.603358
## iter 320 value 331.783614
## iter 330 value 329.627795
## iter 340 value 327.266060
## iter 350 value 325.054886
## iter 360 value 323.434266
## iter 370 value 321.904966
## iter 380 value 320.446616
## iter 390 value 318.934533
## iter 400 value 317.335452
## iter 410 value 316.120889
## iter 420 value 314.927469
## iter 430 value 313.609442
## iter 440 value 312.638502
## iter 450 value 311.887785
## iter 460 value 311.300014
## iter 470 value 310.848868
## iter 480 value 310.418241
## iter 490 value 310.237849
## iter 500 value 310.019176
## final value 310.019176
## stopped after 500 iterations
## # weights: 25
## initial value 1416069.583389
## iter 10 value 2238.202111
## iter 20 value 1738.803993
## iter 30 value 1284.351022
## iter 40 value 1131.389143
## iter 50 value 1081.814527
## iter 60 value 1061.382319
## iter 70 value 1048.601237
## iter 80 value 990.430776
## iter 90 value 972.012073
## iter 100 value 967.280400
## iter 110 value 966.332469
## iter 120 value 965.780017
## iter 130 value 959.377795
## iter 140 value 956.241841
## iter 150 value 954.355821
## iter 160 value 954.296917
## iter 170 value 953.246912
## iter 180 value 952.741974
## iter 190 value 952.704013
## iter 200 value 949.971875
## iter 210 value 949.008291
## iter 220 value 948.944734
## iter 230 value 947.015530
## iter 240 value 946.247923
## iter 250 value 945.320073
## iter 260 value 944.904092
## iter 270 value 944.886916
## iter 280 value 944.499011
## iter 290 value 944.033353
## iter 300 value 943.661728
## iter 310 value 943.579053
## iter 320 value 943.577984
## iter 330 value 943.441042
## iter 340 value 943.422131
## iter 350 value 943.418132
## final value 943.417967
## converged
## # weights: 61
## initial value 1365216.099806
## iter 10 value 6421.776890
## iter 20 value 2312.000363
## iter 30 value 1819.398583
## iter 40 value 1502.520580
## iter 50 value 1383.553475
## iter 60 value 1278.944868
## iter 70 value 1207.198119
## iter 80 value 1161.079270
## iter 90 value 1153.772345
## iter 100 value 1114.059747
## iter 110 value 1062.468245
## iter 120 value 1001.220238
## iter 130 value 994.606825
## iter 140 value 992.781361
## iter 150 value 991.429252
## iter 160 value 989.304512
## iter 170 value 987.049758
## iter 180 value 983.614944
## iter 190 value 981.419372
## iter 200 value 976.255426
## iter 210 value 969.667827
## iter 220 value 963.887997
## iter 230 value 938.604985
## iter 240 value 901.043385
## iter 250 value 873.868881
## iter 260 value 822.605610
## iter 270 value 772.383496
## iter 280 value 740.002720
## iter 290 value 735.719149
## iter 300 value 732.670863
## iter 310 value 731.429551
## iter 320 value 730.310025
## iter 330 value 730.305342
## iter 340 value 730.302079
## final value 730.301941
## converged
## # weights: 121
## initial value 1403325.763243
## iter 10 value 1122.487686
## iter 20 value 870.227515
## iter 30 value 727.204114
## iter 40 value 619.104864
## iter 50 value 578.628219
## iter 60 value 521.944199
## iter 70 value 480.942835
## iter 80 value 435.703695
## iter 90 value 415.579434
## iter 100 value 399.878136
## iter 110 value 383.483034
## iter 120 value 366.345877
## iter 130 value 353.867708
## iter 140 value 346.336705
## iter 150 value 340.493317
## iter 160 value 338.023993
## iter 170 value 336.001899
## iter 180 value 328.304142
## iter 190 value 314.658477
## iter 200 value 307.344009
## iter 210 value 303.593217
## iter 220 value 300.228981
## iter 230 value 297.636419
## iter 240 value 294.041175
## iter 250 value 291.364961
## iter 260 value 288.737225
## iter 270 value 286.071399
## iter 280 value 281.079543
## iter 290 value 277.858500
## iter 300 value 273.132394
## iter 310 value 267.807826
## iter 320 value 263.389189
## iter 330 value 261.578534
## iter 340 value 259.530114
## iter 350 value 256.177391
## iter 360 value 254.380968
## iter 370 value 252.597780
## iter 380 value 250.710564
## iter 390 value 249.421687
## iter 400 value 248.534595
## iter 410 value 248.066951
## iter 420 value 247.662992
## iter 430 value 246.979507
## iter 440 value 246.025056
## iter 450 value 245.623730
## iter 460 value 245.473853
## iter 470 value 245.422949
## iter 480 value 245.414909
## iter 490 value 245.408678
## iter 500 value 245.407584
## final value 245.407584
## stopped after 500 iterations
## # weights: 181
## initial value 1361206.670699
## iter 10 value 1295.858029
## iter 20 value 828.104091
## iter 30 value 640.038740
## iter 40 value 509.873449
## iter 50 value 418.032066
## iter 60 value 364.276351
## iter 70 value 305.188981
## iter 80 value 270.269344
## iter 90 value 242.275767
## iter 100 value 226.337498
## iter 110 value 214.348055
## iter 120 value 203.248665
## iter 130 value 196.357158
## iter 140 value 192.667514
## iter 150 value 188.640037
## iter 160 value 185.457041
## iter 170 value 182.851720
## iter 180 value 180.653876
## iter 190 value 177.588265
## iter 200 value 174.258912
## iter 210 value 169.930911
## iter 220 value 167.074952
## iter 230 value 166.433534
## iter 240 value 165.095074
## iter 250 value 163.829953
## iter 260 value 161.979264
## iter 270 value 160.882454
## iter 280 value 160.018837
## iter 290 value 158.952691
## iter 300 value 157.805368
## iter 310 value 156.446891
## iter 320 value 154.915213
## iter 330 value 152.030485
## iter 340 value 149.456178
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## iter 380 value 145.483806
## iter 390 value 145.313174
## iter 400 value 145.157224
## iter 410 value 143.826943
## iter 420 value 143.375155
## iter 430 value 143.035746
## iter 440 value 141.406108
## iter 450 value 138.713330
## iter 460 value 136.786968
## iter 470 value 134.950843
## iter 480 value 133.732260
## iter 490 value 132.706544
## iter 500 value 132.037020
## final value 132.037020
## stopped after 500 iterations
## # weights: 241
## initial value 1354588.740780
## iter 10 value 1082.956447
## iter 20 value 749.391734
## iter 30 value 612.049103
## iter 40 value 468.374614
## iter 50 value 360.245307
## iter 60 value 296.264092
## iter 70 value 263.289819
## iter 80 value 218.002100
## iter 90 value 184.676417
## iter 100 value 153.140980
## iter 110 value 125.636393
## iter 120 value 103.308894
## iter 130 value 90.000521
## iter 140 value 82.016647
## iter 150 value 76.519829
## iter 160 value 71.715830
## iter 170 value 66.620242
## iter 180 value 61.051333
## iter 190 value 56.394968
## iter 200 value 53.587763
## iter 210 value 50.891358
## iter 220 value 49.009867
## iter 230 value 47.600313
## iter 240 value 46.454343
## iter 250 value 45.080640
## iter 260 value 43.992991
## iter 270 value 42.643544
## iter 280 value 41.306902
## iter 290 value 40.335477
## iter 300 value 39.757244
## iter 310 value 39.117204
## iter 320 value 38.510405
## iter 330 value 37.651777
## iter 340 value 36.855159
## iter 350 value 36.087352
## iter 360 value 35.567927
## iter 370 value 35.228022
## iter 380 value 34.884248
## iter 390 value 34.511737
## iter 400 value 33.995170
## iter 410 value 33.054014
## iter 420 value 32.118513
## iter 430 value 31.499032
## iter 440 value 31.066530
## iter 450 value 30.775875
## iter 460 value 30.589733
## iter 470 value 30.410489
## iter 480 value 30.152226
## iter 490 value 30.049611
## iter 500 value 29.988686
## final value 29.988686
## stopped after 500 iterations
## # weights: 25
## initial value 1433122.288894
## iter 10 value 9355.237341
## iter 20 value 3640.491481
## iter 30 value 1805.757171
## iter 40 value 1470.570940
## iter 50 value 1403.186617
## iter 60 value 1364.137259
## iter 70 value 1272.479221
## iter 80 value 1263.912584
## iter 90 value 1262.800666
## iter 100 value 1262.531084
## iter 110 value 1258.487066
## iter 120 value 1255.504697
## iter 130 value 1253.804049
## iter 140 value 1253.512088
## iter 150 value 1253.091456
## iter 160 value 1252.552893
## iter 170 value 1251.709633
## iter 180 value 1240.958941
## iter 190 value 1237.037129
## iter 200 value 1236.678964
## iter 210 value 1236.206232
## iter 220 value 1236.187591
## iter 230 value 1235.936937
## iter 240 value 1235.795204
## iter 250 value 1235.176899
## iter 260 value 1235.056227
## iter 270 value 1235.048436
## iter 280 value 1235.007871
## iter 290 value 1234.931889
## iter 300 value 1234.865749
## iter 300 value 1234.865747
## final value 1234.865640
## converged
## # weights: 61
## initial value 1409890.577075
## iter 10 value 57180.463238
## iter 20 value 19955.574565
## iter 30 value 15381.741605
## iter 40 value 9944.453119
## iter 50 value 6119.117484
## iter 60 value 3519.940325
## iter 70 value 1719.690705
## iter 80 value 1164.410276
## iter 90 value 1049.969774
## iter 100 value 943.399915
## iter 110 value 890.761131
## iter 120 value 859.182661
## iter 130 value 854.912781
## iter 140 value 852.989679
## iter 150 value 849.802608
## iter 160 value 835.907639
## iter 170 value 816.333326
## iter 180 value 804.644243
## iter 190 value 796.343339
## iter 200 value 780.249106
## iter 210 value 767.763435
## iter 220 value 757.905823
## iter 230 value 754.430889
## iter 240 value 746.911441
## iter 250 value 743.980767
## iter 260 value 743.772738
## iter 270 value 743.202272
## iter 280 value 742.293783
## iter 290 value 740.824125
## iter 300 value 739.595650
## iter 310 value 738.275292
## iter 320 value 737.634340
## iter 330 value 736.528374
## iter 340 value 735.798473
## iter 350 value 735.537174
## iter 360 value 735.086464
## iter 370 value 733.618209
## iter 380 value 733.280630
## iter 390 value 733.150795
## iter 400 value 732.861152
## iter 410 value 732.427658
## iter 420 value 729.692731
## iter 430 value 726.548475
## iter 440 value 721.240876
## iter 450 value 717.506936
## iter 460 value 716.112585
## iter 470 value 715.440512
## iter 480 value 715.150605
## iter 490 value 714.246981
## iter 500 value 713.665812
## final value 713.665812
## stopped after 500 iterations
## # weights: 121
## initial value 1435570.100722
## iter 10 value 161003.576662
## iter 20 value 9210.730501
## iter 30 value 5644.791893
## iter 40 value 5100.405213
## iter 50 value 4184.659583
## iter 60 value 3372.636716
## iter 70 value 2611.047442
## iter 80 value 2153.274016
## iter 90 value 1660.864843
## iter 100 value 1261.213830
## iter 110 value 952.094075
## iter 120 value 899.364230
## iter 130 value 862.149083
## iter 140 value 836.689643
## iter 150 value 824.529595
## iter 160 value 819.746219
## iter 170 value 806.845982
## iter 180 value 800.717655
## iter 190 value 791.382667
## iter 200 value 789.265642
## iter 210 value 783.871317
## iter 220 value 773.693582
## iter 230 value 761.720787
## iter 240 value 755.247007
## iter 250 value 749.786105
## iter 260 value 733.062404
## iter 270 value 709.629572
## iter 280 value 702.718498
## iter 290 value 701.980737
## iter 300 value 695.450951
## iter 310 value 691.175581
## iter 320 value 679.606961
## iter 330 value 668.683713
## iter 340 value 659.455739
## iter 350 value 654.392772
## iter 360 value 652.260823
## iter 370 value 650.818736
## iter 380 value 647.731048
## iter 390 value 640.141321
## iter 400 value 627.817770
## iter 410 value 615.694426
## iter 420 value 609.029886
## iter 430 value 605.829979
## iter 440 value 601.425851
## iter 450 value 601.026466
## iter 460 value 598.354456
## iter 470 value 589.610583
## iter 480 value 574.158178
## iter 490 value 565.574964
## iter 500 value 543.570045
## final value 543.570045
## stopped after 500 iterations
## # weights: 181
## initial value 1393214.604692
## iter 10 value 1044.378032
## iter 20 value 833.073995
## iter 30 value 693.406663
## iter 40 value 540.787301
## iter 50 value 431.685996
## iter 60 value 364.670579
## iter 70 value 320.152970
## iter 80 value 278.274023
## iter 90 value 248.736476
## iter 100 value 225.284833
## iter 110 value 212.190219
## iter 120 value 201.809786
## iter 130 value 193.461049
## iter 140 value 184.016035
## iter 150 value 173.470433
## iter 160 value 163.409213
## iter 170 value 153.234069
## iter 180 value 146.096020
## iter 190 value 141.746530
## iter 200 value 136.417572
## iter 210 value 132.422299
## iter 220 value 129.073834
## iter 230 value 127.036761
## iter 240 value 125.936890
## iter 250 value 124.534620
## iter 260 value 123.106331
## iter 270 value 122.017066
## iter 280 value 121.425048
## iter 290 value 120.018060
## iter 300 value 117.692930
## iter 310 value 115.661008
## iter 320 value 114.833395
## iter 330 value 113.811333
## iter 340 value 112.841419
## iter 350 value 112.024893
## iter 360 value 111.544760
## iter 370 value 111.269446
## iter 380 value 111.166228
## iter 390 value 110.931430
## iter 400 value 110.737485
## iter 410 value 110.418591
## iter 420 value 110.165328
## iter 430 value 109.880438
## iter 440 value 109.605522
## iter 450 value 109.418351
## iter 460 value 109.028662
## iter 470 value 108.095283
## iter 480 value 107.550481
## iter 490 value 107.257003
## iter 500 value 106.846898
## final value 106.846898
## stopped after 500 iterations
## # weights: 241
## initial value 1375678.698071
## iter 10 value 1633.474674
## iter 20 value 853.425435
## iter 30 value 677.816412
## iter 40 value 555.245483
## iter 50 value 447.420550
## iter 60 value 379.860769
## iter 70 value 337.310393
## iter 80 value 300.906447
## iter 90 value 267.619889
## iter 100 value 230.193146
## iter 110 value 194.239187
## iter 120 value 171.994263
## iter 130 value 156.149686
## iter 140 value 145.085650
## iter 150 value 132.480395
## iter 160 value 122.601386
## iter 170 value 117.600712
## iter 180 value 112.173946
## iter 190 value 105.293102
## iter 200 value 98.212996
## iter 210 value 93.171072
## iter 220 value 86.522761
## iter 230 value 81.169382
## iter 240 value 74.650970
## iter 250 value 70.013466
## iter 260 value 65.499664
## iter 270 value 59.669075
## iter 280 value 56.216714
## iter 290 value 54.570129
## iter 300 value 52.816969
## iter 310 value 50.279015
## iter 320 value 48.903057
## iter 330 value 47.876006
## iter 340 value 46.914086
## iter 350 value 45.868277
## iter 360 value 45.160162
## iter 370 value 44.577873
## iter 380 value 44.090507
## iter 390 value 43.633454
## iter 400 value 42.974325
## iter 410 value 42.539649
## iter 420 value 42.044556
## iter 430 value 41.526548
## iter 440 value 40.983889
## iter 450 value 40.330356
## iter 460 value 39.703912
## iter 470 value 39.173774
## iter 480 value 38.565787
## iter 490 value 38.224971
## iter 500 value 38.128785
## final value 38.128785
## stopped after 500 iterations
## # weights: 25
## initial value 1393796.053730
## iter 10 value 18857.730552
## iter 20 value 9089.766386
## iter 30 value 5218.038485
## iter 40 value 4301.270954
## iter 50 value 3431.792342
## iter 60 value 1713.123297
## iter 70 value 1172.351039
## iter 80 value 1097.952453
## iter 90 value 1091.164129
## iter 100 value 1085.692328
## iter 110 value 1068.756385
## iter 120 value 1062.486142
## iter 130 value 1060.163439
## iter 140 value 1059.731593
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## iter 170 value 1056.881196
## iter 180 value 1052.158112
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## iter 200 value 1040.531089
## iter 210 value 1027.221111
## iter 220 value 1027.168105
## iter 230 value 1025.658111
## iter 240 value 1022.985628
## iter 250 value 1021.218586
## iter 260 value 1020.936926
## final value 1020.936237
## converged
## # weights: 61
## initial value 1415135.101805
## iter 10 value 68135.086655
## iter 20 value 21209.036248
## iter 30 value 13634.985240
## iter 40 value 6768.509531
## iter 50 value 3695.247003
## iter 60 value 1831.902660
## iter 70 value 1320.895551
## iter 80 value 1118.088027
## iter 90 value 946.404895
## iter 100 value 874.382493
## iter 110 value 839.034551
## iter 120 value 819.364899
## iter 130 value 807.937227
## iter 140 value 804.877290
## iter 150 value 800.444953
## iter 160 value 784.667986
## iter 170 value 764.288047
## iter 180 value 753.578938
## iter 190 value 746.387702
## iter 200 value 739.714346
## iter 210 value 731.673786
## iter 220 value 726.625235
## iter 230 value 724.761479
## iter 240 value 723.679464
## iter 250 value 723.314921
## iter 260 value 723.285385
## iter 270 value 723.206132
## iter 280 value 723.147279
## iter 290 value 722.880587
## iter 300 value 721.488307
## iter 310 value 720.387845
## iter 320 value 718.863056
## iter 330 value 716.557346
## iter 340 value 715.053585
## iter 350 value 713.455378
## iter 360 value 712.764950
## iter 370 value 712.385598
## iter 380 value 712.357904
## iter 390 value 712.348027
## iter 400 value 712.315085
## iter 410 value 712.256688
## iter 420 value 711.981544
## iter 430 value 711.152699
## iter 440 value 710.165735
## iter 450 value 708.604658
## iter 460 value 707.418965
## iter 470 value 706.055333
## iter 480 value 704.572061
## iter 490 value 702.579047
## iter 500 value 702.118509
## final value 702.118509
## stopped after 500 iterations
## # weights: 121
## initial value 1400987.024934
## iter 10 value 2135.882190
## iter 20 value 948.926987
## iter 30 value 797.008111
## iter 40 value 689.331918
## iter 50 value 612.213711
## iter 60 value 582.466377
## iter 70 value 562.095010
## iter 80 value 544.370207
## iter 90 value 531.111514
## iter 100 value 518.684089
## iter 110 value 500.508092
## iter 120 value 484.879469
## iter 130 value 473.836759
## iter 140 value 465.796593
## iter 150 value 453.666833
## iter 160 value 444.400303
## iter 170 value 433.039128
## iter 180 value 426.691413
## iter 190 value 425.033926
## iter 200 value 422.940677
## iter 210 value 421.821123
## iter 220 value 420.477742
## iter 230 value 418.530599
## iter 240 value 416.470819
## iter 250 value 415.611406
## iter 260 value 415.498186
## iter 270 value 415.315141
## iter 280 value 414.949833
## iter 290 value 414.263174
## iter 300 value 413.038945
## iter 310 value 411.580138
## iter 320 value 409.121648
## iter 330 value 408.317525
## iter 340 value 407.762358
## iter 350 value 407.240295
## iter 360 value 406.829622
## iter 370 value 406.320458
## iter 380 value 405.916276
## iter 390 value 405.471216
## iter 400 value 405.036929
## iter 410 value 404.647924
## iter 420 value 404.290162
## iter 430 value 404.063425
## iter 440 value 403.846852
## iter 450 value 403.377675
## iter 460 value 403.333689
## iter 470 value 403.212174
## iter 480 value 402.473589
## iter 490 value 401.229760
## iter 500 value 399.788727
## final value 399.788727
## stopped after 500 iterations
## # weights: 181
## initial value 1389588.623932
## iter 10 value 1209.034656
## iter 20 value 800.501744
## iter 30 value 684.614919
## iter 40 value 548.261747
## iter 50 value 432.383495
## iter 60 value 370.828258
## iter 70 value 330.718987
## iter 80 value 276.394771
## iter 90 value 256.048623
## iter 100 value 239.096238
## iter 110 value 221.471541
## iter 120 value 205.469551
## iter 130 value 190.934949
## iter 140 value 181.089548
## iter 150 value 171.966430
## iter 160 value 167.114139
## iter 170 value 163.113775
## iter 180 value 158.923377
## iter 190 value 153.316265
## iter 200 value 148.112929
## iter 210 value 139.568078
## iter 220 value 136.039934
## iter 230 value 133.507435
## iter 240 value 131.004745
## iter 250 value 127.530518
## iter 260 value 124.556737
## iter 270 value 122.396522
## iter 280 value 117.948618
## iter 290 value 114.622309
## iter 300 value 111.884977
## iter 310 value 109.583676
## iter 320 value 107.179843
## iter 330 value 104.970756
## iter 340 value 102.789709
## iter 350 value 99.197505
## iter 360 value 95.556480
## iter 370 value 94.537676
## iter 380 value 94.119222
## iter 390 value 93.530980
## iter 400 value 92.677419
## iter 410 value 92.022061
## iter 420 value 90.625679
## iter 430 value 88.928104
## iter 440 value 87.474531
## iter 450 value 86.308564
## iter 460 value 85.511496
## iter 470 value 84.148714
## iter 480 value 82.820092
## iter 490 value 81.692135
## iter 500 value 81.063562
## final value 81.063562
## stopped after 500 iterations
## # weights: 241
## initial value 1482965.235766
## iter 10 value 1180.879252
## iter 20 value 814.111251
## iter 30 value 659.409591
## iter 40 value 579.276728
## iter 50 value 463.478803
## iter 60 value 339.511355
## iter 70 value 287.928845
## iter 80 value 247.352125
## iter 90 value 212.411769
## iter 100 value 178.425787
## iter 110 value 152.024930
## iter 120 value 139.148086
## iter 130 value 126.609525
## iter 140 value 114.505692
## iter 150 value 105.546412
## iter 160 value 99.983455
## iter 170 value 94.341533
## iter 180 value 89.267502
## iter 190 value 84.656162
## iter 200 value 81.053352
## iter 210 value 76.797241
## iter 220 value 73.249086
## iter 230 value 69.026442
## iter 240 value 64.938552
## iter 250 value 60.299851
## iter 260 value 54.017430
## iter 270 value 49.329686
## iter 280 value 46.182784
## iter 290 value 42.967297
## iter 300 value 39.851089
## iter 310 value 37.516642
## iter 320 value 35.625278
## iter 330 value 33.741665
## iter 340 value 32.275883
## iter 350 value 30.241303
## iter 360 value 29.237024
## iter 370 value 27.588382
## iter 380 value 26.333296
## iter 390 value 25.578765
## iter 400 value 24.829598
## iter 410 value 23.625415
## iter 420 value 22.672725
## iter 430 value 21.667207
## iter 440 value 21.032421
## iter 450 value 20.646403
## iter 460 value 20.188280
## iter 470 value 19.845840
## iter 480 value 19.555142
## iter 490 value 19.415572
## iter 500 value 19.351543
## final value 19.351543
## stopped after 500 iterations
## # weights: 25
## initial value 1385756.953187
## iter 10 value 48172.563992
## iter 20 value 5626.046095
## iter 30 value 4827.184454
## iter 40 value 4684.994733
## iter 50 value 4676.847180
## iter 60 value 4411.624885
## iter 70 value 4337.236652
## iter 80 value 4305.803371
## iter 90 value 4189.023750
## iter 100 value 4111.053620
## iter 110 value 4097.388796
## iter 120 value 4077.868661
## iter 130 value 4058.186724
## iter 140 value 4024.363131
## iter 150 value 4001.599711
## iter 160 value 3992.031933
## iter 170 value 3985.928854
## iter 180 value 3844.621387
## iter 190 value 3731.034289
## iter 200 value 3079.219451
## iter 210 value 2651.403268
## iter 220 value 1869.076982
## iter 230 value 1447.981206
## iter 240 value 1348.279574
## iter 250 value 1315.701274
## iter 260 value 1311.012222
## iter 270 value 1296.442451
## iter 280 value 1286.097193
## iter 290 value 1282.492933
## iter 300 value 1280.901038
## iter 310 value 1280.461069
## iter 320 value 1280.174577
## iter 330 value 1278.815732
## iter 340 value 1277.895013
## iter 350 value 1277.342544
## iter 360 value 1277.152740
## final value 1277.152420
## converged
## # weights: 61
## initial value 1399122.577923
## iter 10 value 8492.495248
## iter 20 value 3737.440331
## iter 30 value 3164.521992
## iter 40 value 2751.922036
## iter 50 value 2329.764530
## iter 60 value 1958.142304
## iter 70 value 1797.442093
## iter 80 value 1754.922723
## iter 90 value 1736.232238
## iter 100 value 1695.533168
## iter 110 value 1665.867370
## iter 120 value 1661.013312
## iter 130 value 1658.112296
## iter 140 value 1625.844994
## iter 150 value 1467.383800
## iter 160 value 1287.902896
## iter 170 value 1170.546110
## iter 180 value 1132.947532
## iter 190 value 1105.897044
## iter 200 value 1067.354388
## iter 210 value 1050.529468
## iter 220 value 1040.752608
## iter 230 value 1033.392759
## iter 240 value 1025.014724
## iter 250 value 1020.459613
## iter 260 value 1019.574775
## iter 270 value 1013.798544
## iter 280 value 1011.793458
## iter 290 value 1011.217186
## iter 300 value 1009.965131
## iter 310 value 1008.592208
## iter 320 value 1007.325138
## iter 330 value 1005.693383
## iter 340 value 1005.025584
## iter 350 value 1004.531955
## iter 360 value 1004.318232
## iter 370 value 1003.417727
## iter 380 value 1002.715534
## iter 390 value 1002.463412
## iter 400 value 1002.380161
## final value 1002.380055
## converged
## # weights: 121
## initial value 1413298.565646
## iter 10 value 1430.562028
## iter 20 value 831.031857
## iter 30 value 658.690126
## iter 40 value 549.857548
## iter 50 value 492.301158
## iter 60 value 457.480271
## iter 70 value 433.811995
## iter 80 value 416.642943
## iter 90 value 398.975130
## iter 100 value 381.526442
## iter 110 value 361.337671
## iter 120 value 340.486234
## iter 130 value 330.183381
## iter 140 value 317.941881
## iter 150 value 309.763997
## iter 160 value 301.814000
## iter 170 value 296.264440
## iter 180 value 292.844882
## iter 190 value 288.592533
## iter 200 value 284.673743
## iter 210 value 278.572477
## iter 220 value 273.092553
## iter 230 value 265.569153
## iter 240 value 259.581992
## iter 250 value 257.438313
## iter 260 value 256.415332
## iter 270 value 253.732371
## iter 280 value 250.500840
## iter 290 value 247.289329
## iter 300 value 244.434309
## iter 310 value 241.818178
## iter 320 value 239.240053
## iter 330 value 236.289890
## iter 340 value 230.583178
## iter 350 value 224.751983
## iter 360 value 222.301082
## iter 370 value 220.534332
## iter 380 value 219.531307
## iter 390 value 219.156432
## iter 400 value 218.913938
## iter 410 value 218.709599
## iter 420 value 218.642769
## iter 430 value 218.467114
## iter 440 value 218.375785
## iter 450 value 218.158281
## iter 460 value 218.078756
## iter 470 value 217.957158
## iter 480 value 217.891003
## iter 490 value 217.872053
## iter 500 value 217.871202
## final value 217.871202
## stopped after 500 iterations
## # weights: 181
## initial value 1411394.011152
## iter 10 value 1138.201808
## iter 20 value 771.813077
## iter 30 value 609.921321
## iter 40 value 473.322909
## iter 50 value 382.024387
## iter 60 value 338.760105
## iter 70 value 295.291145
## iter 80 value 251.255562
## iter 90 value 214.970369
## iter 100 value 199.301783
## iter 110 value 188.836892
## iter 120 value 177.175882
## iter 130 value 167.993402
## iter 140 value 157.269320
## iter 150 value 143.186740
## iter 160 value 129.694431
## iter 170 value 121.078541
## iter 180 value 111.482796
## iter 190 value 102.003307
## iter 200 value 96.673977
## iter 210 value 91.251334
## iter 220 value 85.943837
## iter 230 value 82.719689
## iter 240 value 79.065838
## iter 250 value 76.309919
## iter 260 value 74.108705
## iter 270 value 73.123644
## iter 280 value 72.172961
## iter 290 value 71.759515
## iter 300 value 71.367803
## iter 310 value 70.968709
## iter 320 value 70.501713
## iter 330 value 70.255363
## iter 340 value 70.122502
## iter 350 value 69.962639
## iter 360 value 69.874514
## iter 370 value 69.825143
## iter 380 value 69.794042
## iter 390 value 69.718004
## iter 400 value 69.618456
## iter 410 value 69.513349
## iter 420 value 69.303430
## iter 430 value 69.182564
## iter 440 value 69.093405
## iter 450 value 68.982104
## iter 460 value 68.868635
## iter 470 value 68.715874
## iter 480 value 68.271789
## iter 490 value 66.847164
## iter 500 value 65.995674
## final value 65.995674
## stopped after 500 iterations
## # weights: 241
## initial value 1442424.607015
## iter 10 value 1308.114413
## iter 20 value 739.043313
## iter 30 value 607.718410
## iter 40 value 519.468246
## iter 50 value 433.357689
## iter 60 value 362.754740
## iter 70 value 324.148434
## iter 80 value 295.712863
## iter 90 value 263.848253
## iter 100 value 240.841996
## iter 110 value 212.429790
## iter 120 value 185.662652
## iter 130 value 164.931948
## iter 140 value 141.721610
## iter 150 value 126.710512
## iter 160 value 117.472936
## iter 170 value 108.069620
## iter 180 value 101.634520
## iter 190 value 90.423121
## iter 200 value 81.015868
## iter 210 value 73.964015
## iter 220 value 67.833524
## iter 230 value 64.017169
## iter 240 value 60.042701
## iter 250 value 56.459999
## iter 260 value 54.295065
## iter 270 value 52.049504
## iter 280 value 49.624346
## iter 290 value 48.404838
## iter 300 value 46.712110
## iter 310 value 44.551690
## iter 320 value 43.024047
## iter 330 value 41.128716
## iter 340 value 39.114657
## iter 350 value 36.356922
## iter 360 value 34.593786
## iter 370 value 33.541986
## iter 380 value 32.857202
## iter 390 value 32.321888
## iter 400 value 31.713845
## iter 410 value 31.328039
## iter 420 value 30.922180
## iter 430 value 30.410621
## iter 440 value 29.946491
## iter 450 value 29.559267
## iter 460 value 29.084275
## iter 470 value 28.642889
## iter 480 value 28.374166
## iter 490 value 28.220594
## iter 500 value 28.154890
## final value 28.154890
## stopped after 500 iterations
## # weights: 25
## initial value 1410405.784373
## iter 10 value 17593.289730
## iter 20 value 15479.109890
## iter 30 value 14370.467707
## iter 40 value 13529.388845
## iter 50 value 8311.475210
## iter 60 value 3074.308761
## iter 70 value 1828.148546
## iter 80 value 1447.956967
## iter 90 value 1387.715565
## iter 100 value 1338.917764
## iter 110 value 1283.525839
## iter 120 value 1279.470971
## iter 130 value 1279.434373
## iter 130 value 1279.434362
## final value 1279.434362
## converged
## # weights: 61
## initial value 1390495.319656
## iter 10 value 4262.310965
## iter 20 value 3135.835201
## iter 30 value 2607.914753
## iter 40 value 2294.598266
## iter 50 value 1947.171829
## iter 60 value 1749.449614
## iter 70 value 1682.371193
## iter 80 value 1570.643663
## iter 90 value 1369.438522
## iter 100 value 1238.022184
## iter 110 value 1114.010623
## iter 120 value 1048.198666
## iter 130 value 1024.750346
## iter 140 value 1007.624594
## iter 150 value 965.220398
## iter 160 value 904.860688
## iter 170 value 876.086587
## iter 180 value 851.829014
## iter 190 value 819.365860
## iter 200 value 786.670386
## iter 210 value 774.455769
## iter 220 value 764.041146
## iter 230 value 755.453109
## iter 240 value 751.936619
## iter 250 value 749.143105
## iter 260 value 748.600057
## iter 270 value 747.053135
## iter 280 value 742.237694
## iter 290 value 736.071864
## iter 300 value 732.123105
## iter 310 value 731.152801
## iter 320 value 730.546548
## iter 330 value 730.496267
## iter 340 value 730.494612
## final value 730.494297
## converged
## # weights: 121
## initial value 1428212.309370
## iter 10 value 3686.633408
## iter 20 value 1838.579675
## iter 30 value 1330.776320
## iter 40 value 1189.277562
## iter 50 value 1096.744237
## iter 60 value 1055.879668
## iter 70 value 1014.891592
## iter 80 value 976.702369
## iter 90 value 941.322410
## iter 100 value 866.271912
## iter 110 value 817.432624
## iter 120 value 784.615994
## iter 130 value 754.499144
## iter 140 value 728.037059
## iter 150 value 711.028241
## iter 160 value 696.247781
## iter 170 value 681.102139
## iter 180 value 668.257284
## iter 190 value 660.414762
## iter 200 value 641.482853
## iter 210 value 626.642362
## iter 220 value 613.256818
## iter 230 value 606.607440
## iter 240 value 597.809778
## iter 250 value 595.791958
## iter 260 value 594.046556
## iter 270 value 592.251462
## iter 280 value 590.834968
## iter 290 value 589.602787
## iter 300 value 588.554477
## iter 310 value 585.820644
## iter 320 value 582.637223
## iter 330 value 580.338698
## iter 340 value 578.697859
## iter 350 value 575.668634
## iter 360 value 571.347476
## iter 370 value 568.293413
## iter 380 value 564.898680
## iter 390 value 564.071284
## iter 400 value 563.636708
## iter 410 value 563.228502
## iter 420 value 563.144202
## iter 430 value 563.128731
## iter 440 value 563.125472
## iter 450 value 563.124620
## final value 563.124485
## converged
## # weights: 181
## initial value 1345794.900611
## iter 10 value 1269.060245
## iter 20 value 861.238077
## iter 30 value 708.115266
## iter 40 value 632.558192
## iter 50 value 566.335817
## iter 60 value 530.295342
## iter 70 value 494.268841
## iter 80 value 475.722849
## iter 90 value 461.686500
## iter 100 value 449.808581
## iter 110 value 438.999394
## iter 120 value 432.328629
## iter 130 value 427.636549
## iter 140 value 424.115737
## iter 150 value 419.410500
## iter 160 value 415.638753
## iter 170 value 412.910816
## iter 180 value 410.519073
## iter 190 value 407.769599
## iter 200 value 404.804591
## iter 210 value 401.424497
## iter 220 value 400.311839
## iter 230 value 398.983355
## iter 240 value 397.742900
## iter 250 value 396.350654
## iter 260 value 395.551638
## iter 270 value 394.917512
## iter 280 value 393.951534
## iter 290 value 393.186533
## iter 300 value 392.509012
## iter 310 value 392.024675
## iter 320 value 391.793276
## iter 330 value 391.497790
## iter 340 value 390.992714
## iter 350 value 389.928879
## iter 360 value 388.656630
## iter 370 value 387.838451
## iter 380 value 387.167161
## iter 390 value 386.024954
## iter 400 value 384.929248
## iter 410 value 384.282733
## iter 420 value 383.735951
## iter 430 value 383.369506
## iter 440 value 383.217827
## iter 450 value 383.149137
## iter 460 value 383.063970
## iter 470 value 382.964769
## iter 480 value 382.745338
## iter 490 value 381.873027
## iter 500 value 380.320393
## final value 380.320393
## stopped after 500 iterations
## # weights: 241
## initial value 1442893.265919
## iter 10 value 1865.163565
## iter 20 value 953.755893
## iter 30 value 709.643692
## iter 40 value 602.663354
## iter 50 value 546.960928
## iter 60 value 511.975300
## iter 70 value 488.632974
## iter 80 value 476.636292
## iter 90 value 467.428162
## iter 100 value 460.469860
## iter 110 value 455.010734
## iter 120 value 450.916494
## iter 130 value 444.209310
## iter 140 value 437.537908
## iter 150 value 431.553150
## iter 160 value 425.673790
## iter 170 value 418.708580
## iter 180 value 412.090185
## iter 190 value 403.777857
## iter 200 value 398.676912
## iter 210 value 393.541194
## iter 220 value 389.667371
## iter 230 value 385.144506
## iter 240 value 381.277392
## iter 250 value 376.866504
## iter 260 value 372.120153
## iter 270 value 366.715133
## iter 280 value 360.492842
## iter 290 value 355.775235
## iter 300 value 352.415857
## iter 310 value 349.824220
## iter 320 value 346.198988
## iter 330 value 343.231419
## iter 340 value 340.795564
## iter 350 value 338.678983
## iter 360 value 336.201743
## iter 370 value 334.083586
## iter 380 value 331.356664
## iter 390 value 328.056124
## iter 400 value 325.803056
## iter 410 value 324.324926
## iter 420 value 322.705850
## iter 430 value 321.239330
## iter 440 value 320.085876
## iter 450 value 319.363352
## iter 460 value 318.809280
## iter 470 value 318.312681
## iter 480 value 317.728673
## iter 490 value 317.400780
## iter 500 value 316.925133
## final value 316.925133
## stopped after 500 iterations
## # weights: 25
## initial value 1376505.554393
## iter 10 value 13868.910719
## iter 20 value 8980.535514
## iter 30 value 6038.288622
## iter 40 value 5217.029749
## iter 50 value 5179.685831
## iter 60 value 5080.343651
## iter 70 value 2615.825485
## iter 80 value 1623.733020
## iter 90 value 1383.242726
## iter 100 value 1321.905558
## iter 110 value 1307.565600
## iter 120 value 1305.432616
## iter 130 value 1299.419336
## iter 140 value 1284.128492
## iter 150 value 1249.348190
## iter 160 value 1124.950146
## iter 170 value 1099.944447
## iter 180 value 1090.267213
## iter 190 value 1090.168701
## iter 200 value 1090.153358
## iter 210 value 1089.992062
## final value 1089.809081
## converged
## # weights: 61
## initial value 1408902.183080
## iter 10 value 12056.134414
## iter 20 value 6786.434834
## iter 30 value 4103.598224
## iter 40 value 2740.811109
## iter 50 value 2206.431614
## iter 60 value 1985.524291
## iter 70 value 1895.275355
## iter 80 value 1873.187740
## iter 90 value 1854.557052
## iter 100 value 1689.612057
## iter 110 value 1637.186818
## iter 120 value 1582.134762
## iter 130 value 1572.210134
## iter 140 value 1568.897402
## iter 150 value 1542.925975
## iter 160 value 1502.448869
## iter 170 value 1445.021854
## iter 180 value 1399.038459
## iter 190 value 1374.345424
## iter 200 value 1363.518485
## iter 210 value 1338.337040
## iter 220 value 1290.280435
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## final value 645.396803
## stopped after 500 iterations
## # weights: 121
## initial value 1411255.962266
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## # weights: 181
## initial value 1434844.665530
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## final value 104.196012
## stopped after 500 iterations
## # weights: 241
## initial value 1382697.281020
## iter 10 value 1494.648186
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## final value 54.550072
## stopped after 500 iterations
## # weights: 25
## initial value 1409918.064319
## iter 10 value 16893.263944
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## iter 30 value 12099.583290
## iter 40 value 9192.735113
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## final value 830.196334
## converged
## # weights: 61
## initial value 1408705.009561
## iter 10 value 42011.707047
## iter 20 value 18061.828053
## iter 30 value 12755.860611
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## final value 658.537665
## stopped after 500 iterations
## # weights: 121
## initial value 1472655.813606
## iter 10 value 1955.678658
## iter 20 value 967.138206
## iter 30 value 740.212796
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## final value 222.107644
## stopped after 500 iterations
## # weights: 181
## initial value 1383724.921133
## iter 10 value 1140.656765
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## final value 78.094737
## stopped after 500 iterations
## # weights: 241
## initial value 1394784.426832
## iter 10 value 1253.851802
## iter 20 value 742.479039
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## final value 37.072628
## stopped after 500 iterations
## # weights: 25
## initial value 1407644.724686
## iter 10 value 16277.244852
## iter 20 value 15396.841019
## iter 30 value 14813.892143
## iter 40 value 9830.005938
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## iter 320 value 1117.044745
## iter 330 value 1117.017393
## iter 330 value 1117.017386
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## final value 1117.017377
## converged
## # weights: 61
## initial value 1397988.989015
## iter 10 value 3669.274819
## iter 20 value 2112.660533
## iter 30 value 1640.147688
## iter 40 value 1191.984673
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## final value 528.250943
## stopped after 500 iterations
## # weights: 121
## initial value 1358735.038285
## iter 10 value 2463.261607
## iter 20 value 1216.252666
## iter 30 value 897.187740
## iter 40 value 696.325529
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## final value 346.213570
## stopped after 500 iterations
## # weights: 181
## initial value 1370357.622741
## iter 10 value 1121.493856
## iter 20 value 695.219403
## iter 30 value 581.608939
## iter 40 value 492.097311
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## iter 70 value 358.554775
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## iter 90 value 295.875056
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## iter 500 value 78.791758
## final value 78.791758
## stopped after 500 iterations
## # weights: 241
## initial value 1394531.629793
## iter 10 value 1556.418468
## iter 20 value 728.020973
## iter 30 value 569.930840
## iter 40 value 455.452673
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## iter 90 value 168.119152
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## iter 310 value 38.293568
## iter 320 value 37.293692
## iter 330 value 36.415948
## iter 340 value 35.432999
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## final value 28.274641
## stopped after 500 iterations
## # weights: 25
## initial value 1393612.292566
## iter 10 value 6291.180642
## iter 20 value 6265.542738
## iter 20 value 6265.542688
## iter 20 value 6265.542688
## final value 6265.542688
## converged
## # weights: 61
## initial value 1440695.729584
## iter 10 value 62290.459830
## iter 20 value 4751.281675
## iter 30 value 3775.951060
## iter 40 value 3030.171394
## iter 50 value 1510.024531
## iter 60 value 1357.852890
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## iter 80 value 1073.131229
## iter 90 value 988.031675
## iter 100 value 953.456285
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## iter 120 value 917.985774
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## iter 140 value 873.971712
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## iter 170 value 765.278573
## iter 180 value 755.006050
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## iter 200 value 745.743410
## iter 210 value 736.405121
## iter 220 value 724.966776
## iter 230 value 716.342378
## iter 240 value 713.736118
## iter 250 value 711.474880
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## iter 270 value 704.791343
## iter 280 value 700.433575
## iter 290 value 697.728291
## iter 300 value 695.677807
## iter 310 value 695.228944
## iter 320 value 694.280494
## iter 330 value 693.561119
## iter 340 value 691.330622
## iter 350 value 688.668165
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## iter 390 value 678.883741
## iter 400 value 666.755233
## iter 410 value 642.387422
## iter 420 value 634.509208
## iter 430 value 634.455179
## iter 440 value 633.785330
## iter 450 value 632.290941
## iter 460 value 629.927131
## iter 470 value 627.166726
## iter 480 value 627.104873
## iter 490 value 626.487275
## iter 500 value 622.075383
## final value 622.075383
## stopped after 500 iterations
## # weights: 121
## initial value 1390036.315580
## iter 10 value 2272.500271
## iter 20 value 938.577558
## iter 30 value 764.399625
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## final value 280.281032
## stopped after 500 iterations
## # weights: 181
## initial value 1406500.058150
## iter 10 value 1205.902608
## iter 20 value 808.220355
## iter 30 value 650.812361
## iter 40 value 529.300351
## iter 50 value 441.835262
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## final value 99.567269
## stopped after 500 iterations
## # weights: 241
## initial value 1425732.097986
## iter 10 value 1375.640186
## iter 20 value 821.895031
## iter 30 value 674.939125
## iter 40 value 502.232512
## iter 50 value 429.798325
## iter 60 value 358.629293
## iter 70 value 307.894247
## iter 80 value 271.940674
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## iter 100 value 224.864809
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## iter 200 value 136.443674
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## iter 220 value 119.114537
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## iter 270 value 94.091510
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## iter 390 value 50.253663
## iter 400 value 48.655191
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## iter 460 value 42.610683
## iter 470 value 42.158281
## iter 480 value 41.509030
## iter 490 value 41.182159
## iter 500 value 41.096494
## final value 41.096494
## stopped after 500 iterations
## # weights: 25
## initial value 1364156.587034
## iter 10 value 19539.964640
## iter 20 value 14785.573082
## iter 30 value 11058.799529
## iter 40 value 3463.995804
## iter 50 value 1678.490273
## iter 60 value 1297.823507
## iter 70 value 1179.016997
## iter 80 value 1173.001288
## iter 90 value 1167.044784
## iter 100 value 1148.292159
## iter 110 value 1147.931182
## final value 1147.930339
## converged
## # weights: 61
## initial value 1414575.668548
## iter 10 value 76283.602962
## iter 20 value 35031.985881
## iter 30 value 21693.498478
## iter 40 value 12984.117480
## iter 50 value 7398.826873
## iter 60 value 6909.962394
## iter 70 value 4436.751243
## iter 80 value 2952.111164
## iter 90 value 2105.011744
## iter 100 value 1449.891408
## iter 110 value 1176.898926
## iter 120 value 1092.502129
## iter 130 value 1031.599327
## iter 140 value 993.858163
## iter 150 value 956.603983
## iter 160 value 930.869144
## iter 170 value 918.121027
## iter 180 value 910.244988
## iter 190 value 892.411503
## iter 200 value 870.429695
## iter 210 value 855.490608
## iter 220 value 849.247674
## iter 230 value 846.339099
## iter 240 value 845.042226
## iter 250 value 844.976353
## iter 260 value 844.972309
## final value 844.971867
## converged
## # weights: 121
## initial value 1398789.313064
## iter 10 value 1245.524635
## iter 20 value 931.859082
## iter 30 value 795.605418
## iter 40 value 704.351808
## iter 50 value 651.861833
## iter 60 value 619.390576
## iter 70 value 592.516517
## iter 80 value 570.088424
## iter 90 value 554.220154
## iter 100 value 540.937813
## iter 110 value 528.404300
## iter 120 value 518.983216
## iter 130 value 509.965185
## iter 140 value 508.015754
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## iter 170 value 502.899432
## iter 180 value 501.342660
## iter 190 value 498.254652
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## iter 210 value 489.031889
## iter 220 value 486.915882
## iter 230 value 485.663998
## iter 240 value 482.233454
## iter 250 value 479.707872
## iter 260 value 477.795332
## iter 270 value 474.942479
## iter 280 value 472.789943
## iter 290 value 468.435254
## iter 300 value 463.322175
## iter 310 value 460.870158
## iter 320 value 458.411777
## iter 330 value 456.307730
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## iter 360 value 450.989034
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## iter 390 value 449.746967
## iter 400 value 449.615295
## iter 410 value 449.575150
## iter 420 value 449.560120
## iter 430 value 449.557328
## final value 449.557048
## converged
## # weights: 181
## initial value 1346723.388655
## iter 10 value 1101.374678
## iter 20 value 864.999899
## iter 30 value 739.426692
## iter 40 value 650.496874
## iter 50 value 588.301452
## iter 60 value 555.138072
## iter 70 value 522.899716
## iter 80 value 497.945826
## iter 90 value 484.694735
## iter 100 value 469.009760
## iter 110 value 455.802350
## iter 120 value 449.051663
## iter 130 value 443.844046
## iter 140 value 439.253970
## iter 150 value 435.087032
## iter 160 value 431.127004
## iter 170 value 426.974663
## iter 180 value 420.786428
## iter 190 value 416.027188
## iter 200 value 412.809534
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## iter 220 value 404.630504
## iter 230 value 400.436890
## iter 240 value 395.725722
## iter 250 value 393.608461
## iter 260 value 390.758111
## iter 270 value 387.528131
## iter 280 value 381.836783
## iter 290 value 379.516696
## iter 300 value 376.297230
## iter 310 value 372.710206
## iter 320 value 369.615908
## iter 330 value 366.944220
## iter 340 value 365.069726
## iter 350 value 363.283517
## iter 360 value 361.330166
## iter 370 value 360.345732
## iter 380 value 359.371164
## iter 390 value 357.009300
## iter 400 value 355.587019
## iter 410 value 354.970429
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## iter 440 value 352.916982
## iter 450 value 352.650010
## iter 460 value 352.239208
## iter 470 value 351.409474
## iter 480 value 350.727397
## iter 490 value 350.492440
## iter 500 value 350.430458
## final value 350.430458
## stopped after 500 iterations
## # weights: 241
## initial value 1375346.489162
## iter 10 value 1367.781777
## iter 20 value 952.422485
## iter 30 value 744.106819
## iter 40 value 640.112622
## iter 50 value 580.162631
## iter 60 value 534.425015
## iter 70 value 506.890845
## iter 80 value 481.737665
## iter 90 value 463.292341
## iter 100 value 449.669845
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## iter 210 value 389.588363
## iter 220 value 385.937226
## iter 230 value 382.106459
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## iter 250 value 375.895933
## iter 260 value 372.705938
## iter 270 value 368.916919
## iter 280 value 366.265915
## iter 290 value 361.644001
## iter 300 value 357.251667
## iter 310 value 354.556305
## iter 320 value 352.597746
## iter 330 value 351.230341
## iter 340 value 349.928834
## iter 350 value 348.678311
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## iter 380 value 342.492997
## iter 390 value 340.550433
## iter 400 value 339.090973
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## iter 420 value 337.158985
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## iter 440 value 335.526791
## iter 450 value 334.512118
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## iter 470 value 330.402574
## iter 480 value 327.984257
## iter 490 value 326.813734
## iter 500 value 325.843866
## final value 325.843866
## stopped after 500 iterations
## # weights: 25
## initial value 1394525.925872
## iter 10 value 96591.768306
## iter 20 value 16831.014352
## iter 30 value 15384.630985
## iter 40 value 15083.243450
## iter 50 value 15075.854447
## iter 60 value 15072.182372
## iter 70 value 15065.526987
## iter 80 value 12120.845998
## iter 90 value 8838.201624
## iter 100 value 5509.937832
## iter 110 value 5483.839417
## iter 120 value 5438.417239
## iter 130 value 5380.122125
## iter 140 value 4735.464660
## iter 150 value 1628.060636
## iter 160 value 1453.620316
## iter 170 value 1388.298703
## iter 180 value 1372.723301
## iter 190 value 1367.740539
## iter 200 value 1339.280814
## iter 210 value 1280.134873
## iter 220 value 1276.379470
## iter 230 value 1255.258570
## iter 240 value 1254.302727
## iter 250 value 1253.854252
## iter 260 value 1253.847075
## iter 270 value 1241.281215
## iter 280 value 1232.996042
## iter 290 value 1232.646939
## iter 300 value 1216.368372
## iter 310 value 1207.771213
## iter 320 value 1205.549531
## iter 330 value 1198.297716
## iter 340 value 1181.254359
## iter 350 value 1177.466464
## iter 360 value 1167.810985
## iter 370 value 1167.158281
## final value 1167.157437
## converged
## # weights: 61
## initial value 1382301.841007
## iter 10 value 197526.920235
## iter 20 value 9635.408165
## iter 30 value 4792.720309
## iter 40 value 3680.483923
## iter 50 value 2423.108187
## iter 60 value 1652.898777
## iter 70 value 1281.654353
## iter 80 value 1167.694244
## iter 90 value 1123.592482
## iter 100 value 1061.404972
## iter 110 value 904.521784
## iter 120 value 837.800852
## iter 130 value 804.907375
## iter 140 value 775.416852
## iter 150 value 754.587690
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## iter 180 value 728.253221
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## iter 200 value 714.302200
## iter 210 value 710.181162
## iter 220 value 705.381564
## iter 230 value 700.195031
## iter 240 value 697.287720
## iter 250 value 694.076784
## iter 260 value 688.137707
## iter 270 value 686.072856
## iter 280 value 682.652251
## iter 290 value 676.285247
## iter 300 value 658.730408
## iter 310 value 644.137320
## iter 320 value 639.347843
## iter 330 value 636.097032
## iter 340 value 633.707720
## iter 350 value 632.936031
## iter 360 value 632.581748
## iter 370 value 632.261613
## iter 380 value 631.725310
## iter 390 value 631.661774
## iter 400 value 631.655817
## iter 410 value 631.578135
## iter 420 value 631.372563
## iter 430 value 631.095213
## iter 440 value 630.700405
## iter 450 value 625.254516
## iter 460 value 620.312404
## iter 470 value 618.656560
## iter 480 value 617.849330
## iter 490 value 617.604580
## iter 500 value 617.529045
## final value 617.529045
## stopped after 500 iterations
## # weights: 121
## initial value 1409867.375222
## iter 10 value 1686.405311
## iter 20 value 857.985567
## iter 30 value 660.476272
## iter 40 value 600.954001
## iter 50 value 527.974385
## iter 60 value 470.255145
## iter 70 value 436.553263
## iter 80 value 408.015208
## iter 90 value 390.910751
## iter 100 value 377.906162
## iter 110 value 358.840807
## iter 120 value 345.128001
## iter 130 value 341.036040
## iter 140 value 338.499667
## iter 150 value 326.182967
## iter 160 value 323.129776
## iter 170 value 321.399314
## iter 180 value 318.828754
## iter 190 value 316.899890
## iter 200 value 311.504787
## iter 210 value 308.387322
## iter 220 value 305.132693
## iter 230 value 302.722422
## iter 240 value 300.499990
## iter 250 value 299.968106
## iter 260 value 299.416763
## iter 270 value 298.681857
## iter 280 value 297.886601
## iter 290 value 296.718652
## iter 300 value 295.772848
## iter 310 value 294.240028
## iter 320 value 292.666977
## iter 330 value 291.284409
## iter 340 value 290.598466
## iter 350 value 290.141568
## iter 360 value 289.676749
## iter 370 value 289.432865
## iter 380 value 289.323966
## iter 390 value 289.283227
## iter 400 value 289.256618
## iter 410 value 289.231913
## iter 420 value 289.206883
## iter 430 value 289.196671
## iter 440 value 289.192607
## iter 450 value 289.190660
## iter 460 value 289.189607
## iter 470 value 289.189377
## final value 289.189261
## converged
## # weights: 181
## initial value 1374732.323372
## iter 10 value 993.897666
## iter 20 value 798.551150
## iter 30 value 632.626272
## iter 40 value 516.047781
## iter 50 value 413.495333
## iter 60 value 361.204829
## iter 70 value 328.198015
## iter 80 value 300.822750
## iter 90 value 282.687335
## iter 100 value 253.961233
## iter 110 value 233.658455
## iter 120 value 221.830020
## iter 130 value 212.931586
## iter 140 value 201.027050
## iter 150 value 186.088598
## iter 160 value 177.967819
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## iter 180 value 165.915141
## iter 190 value 162.703550
## iter 200 value 159.956373
## iter 210 value 156.920766
## iter 220 value 154.663669
## iter 230 value 152.834656
## iter 240 value 150.409344
## iter 250 value 148.873334
## iter 260 value 147.628046
## iter 270 value 146.291252
## iter 280 value 145.285251
## iter 290 value 144.403643
## iter 300 value 143.158265
## iter 310 value 141.894921
## iter 320 value 141.327458
## iter 330 value 140.703712
## iter 340 value 139.975127
## iter 350 value 138.948636
## iter 360 value 138.546029
## iter 370 value 138.380350
## iter 380 value 138.331668
## iter 390 value 138.226743
## iter 400 value 138.074051
## iter 410 value 137.916661
## iter 420 value 137.626705
## iter 430 value 137.289046
## iter 440 value 136.764157
## iter 450 value 136.158504
## iter 460 value 135.157178
## iter 470 value 134.636990
## iter 480 value 134.258048
## iter 490 value 134.019353
## iter 500 value 133.541902
## final value 133.541902
## stopped after 500 iterations
## # weights: 241
## initial value 1420191.736214
## iter 10 value 2057.734051
## iter 20 value 894.692453
## iter 30 value 685.520099
## iter 40 value 536.892384
## iter 50 value 376.024034
## iter 60 value 281.758903
## iter 70 value 228.054783
## iter 80 value 199.570915
## iter 90 value 163.913101
## iter 100 value 144.275504
## iter 110 value 131.106369
## iter 120 value 121.123848
## iter 130 value 114.278448
## iter 140 value 108.241403
## iter 150 value 103.128019
## iter 160 value 99.254677
## iter 170 value 94.806304
## iter 180 value 92.242950
## iter 190 value 90.153653
## iter 200 value 88.112457
## iter 210 value 85.922383
## iter 220 value 83.753258
## iter 230 value 82.251870
## iter 240 value 81.010004
## iter 250 value 79.021711
## iter 260 value 76.859333
## iter 270 value 74.715342
## iter 280 value 71.674523
## iter 290 value 68.532800
## iter 300 value 66.781845
## iter 310 value 65.937599
## iter 320 value 65.181016
## iter 330 value 64.670816
## iter 340 value 64.179231
## iter 350 value 63.737232
## iter 360 value 63.245710
## iter 370 value 62.682823
## iter 380 value 62.107532
## iter 390 value 61.398817
## iter 400 value 60.867428
## iter 410 value 60.508152
## iter 420 value 60.185347
## iter 430 value 59.828264
## iter 440 value 59.442172
## iter 450 value 58.960882
## iter 460 value 58.458292
## iter 470 value 57.814036
## iter 480 value 56.972497
## iter 490 value 56.573496
## iter 500 value 56.443051
## final value 56.443051
## stopped after 500 iterations
## # weights: 25
## initial value 1398875.043009
## iter 10 value 8819.870440
## iter 20 value 5882.813063
## iter 30 value 5507.897629
## iter 40 value 5451.451290
## iter 50 value 5403.953066
## iter 60 value 5330.115179
## iter 70 value 5110.688737
## iter 80 value 4432.810618
## iter 90 value 4070.066173
## iter 100 value 3279.990254
## iter 110 value 2551.505919
## iter 120 value 1604.510803
## iter 130 value 1329.459412
## iter 140 value 1286.806778
## iter 150 value 1273.692606
## iter 160 value 1264.217897
## iter 170 value 1238.732191
## iter 180 value 1224.929641
## iter 190 value 1220.530398
## iter 200 value 1218.663529
## iter 210 value 1218.591887
## iter 220 value 1218.241435
## iter 230 value 1216.480308
## iter 240 value 1203.749134
## iter 250 value 1146.453873
## iter 260 value 1146.317447
## iter 270 value 1145.212039
## iter 280 value 1145.068756
## iter 290 value 1142.160198
## iter 300 value 1139.798014
## iter 310 value 1136.672279
## iter 320 value 1136.263102
## iter 330 value 1135.616945
## iter 340 value 1135.514163
## iter 350 value 1134.582958
## iter 360 value 1134.153595
## iter 370 value 1134.149932
## iter 380 value 1133.942022
## iter 390 value 1133.928792
## iter 400 value 1132.141585
## iter 410 value 1131.614865
## iter 420 value 1126.958464
## iter 430 value 1125.260285
## iter 440 value 1124.952637
## iter 450 value 1118.158286
## iter 460 value 1111.113245
## iter 470 value 1101.413731
## iter 480 value 1062.196960
## iter 490 value 1023.187145
## iter 500 value 1017.431136
## final value 1017.431136
## stopped after 500 iterations
## # weights: 61
## initial value 1383047.417602
## iter 10 value 16099.374415
## iter 20 value 11548.531358
## iter 30 value 10546.615096
## iter 40 value 7370.107431
## iter 50 value 3578.066733
## iter 60 value 1626.687570
## iter 70 value 1197.689323
## iter 80 value 1108.770595
## iter 90 value 1012.561383
## iter 100 value 955.221352
## iter 110 value 907.183952
## iter 120 value 871.015366
## iter 130 value 856.692499
## iter 140 value 853.396985
## iter 150 value 845.333414
## iter 160 value 829.640436
## iter 170 value 807.567785
## iter 180 value 792.420814
## iter 190 value 782.573851
## iter 200 value 779.102500
## iter 210 value 777.553948
## iter 220 value 769.918506
## iter 230 value 735.339537
## iter 240 value 714.457560
## iter 250 value 698.699287
## iter 260 value 691.279157
## iter 270 value 688.674901
## iter 280 value 677.039304
## iter 290 value 631.340602
## iter 300 value 613.897297
## iter 310 value 611.082670
## iter 320 value 607.720489
## iter 330 value 606.323803
## iter 340 value 603.018002
## iter 350 value 602.708741
## iter 360 value 600.629701
## iter 370 value 598.185695
## iter 380 value 596.872689
## iter 390 value 592.740234
## iter 400 value 589.894572
## iter 410 value 587.742005
## iter 420 value 586.965381
## iter 430 value 586.584818
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## iter 450 value 586.461489
## iter 460 value 586.432626
## iter 470 value 586.430373
## iter 480 value 586.428895
## iter 490 value 586.424850
## iter 500 value 586.421392
## final value 586.421392
## stopped after 500 iterations
## # weights: 121
## initial value 1370582.836008
## iter 10 value 3631.614799
## iter 20 value 1461.785218
## iter 30 value 937.099507
## iter 40 value 726.762550
## iter 50 value 639.689765
## iter 60 value 583.205764
## iter 70 value 541.290586
## iter 80 value 524.292339
## iter 90 value 506.170388
## iter 100 value 483.930230
## iter 110 value 468.250542
## iter 120 value 454.600133
## iter 130 value 444.927968
## iter 140 value 434.938408
## iter 150 value 413.801984
## iter 160 value 399.180750
## iter 170 value 388.089136
## iter 180 value 359.774126
## iter 190 value 343.964155
## iter 200 value 336.981816
## iter 210 value 329.543988
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## iter 240 value 312.643871
## iter 250 value 310.259713
## iter 260 value 309.329466
## iter 270 value 307.907810
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## iter 290 value 301.788219
## iter 300 value 298.910270
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## iter 320 value 292.159920
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## iter 480 value 271.001493
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## iter 500 value 270.540563
## final value 270.540563
## stopped after 500 iterations
## # weights: 181
## initial value 1434633.464253
## iter 10 value 1211.061852
## iter 20 value 740.764855
## iter 30 value 616.856084
## iter 40 value 503.382613
## iter 50 value 410.970976
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## iter 470 value 83.050730
## iter 480 value 81.595828
## iter 490 value 80.621371
## iter 500 value 80.021326
## final value 80.021326
## stopped after 500 iterations
## # weights: 241
## initial value 1380919.724748
## iter 10 value 1184.693603
## iter 20 value 789.846203
## iter 30 value 626.099619
## iter 40 value 513.622058
## iter 50 value 405.533747
## iter 60 value 352.905578
## iter 70 value 311.974266
## iter 80 value 275.686075
## iter 90 value 242.976254
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## iter 210 value 102.619258
## iter 220 value 97.142019
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## iter 270 value 76.749862
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## iter 420 value 52.784614
## iter 430 value 51.666369
## iter 440 value 51.084932
## iter 450 value 49.981722
## iter 460 value 49.122090
## iter 470 value 48.239312
## iter 480 value 47.552507
## iter 490 value 47.350896
## iter 500 value 47.315523
## final value 47.315523
## stopped after 500 iterations
## # weights: 25
## initial value 1381291.702288
## iter 10 value 19170.296626
## iter 20 value 10929.812398
## iter 30 value 7897.891611
## iter 40 value 6679.587140
## iter 50 value 6334.766106
## iter 60 value 6013.615421
## iter 70 value 5780.300317
## iter 80 value 5769.670774
## iter 90 value 5748.883112
## iter 100 value 4648.913394
## iter 110 value 3996.047390
## iter 120 value 3018.082634
## iter 130 value 1460.402361
## iter 140 value 1232.551252
## iter 150 value 1195.956300
## iter 160 value 1192.217119
## iter 170 value 1169.971604
## iter 180 value 1148.746503
## iter 190 value 1147.355879
## iter 200 value 1146.382529
## iter 210 value 1146.337208
## iter 220 value 1146.196806
## iter 230 value 1145.613721
## iter 240 value 1143.586743
## iter 250 value 1141.448504
## iter 260 value 1141.040413
## iter 270 value 1139.757698
## iter 280 value 1139.181993
## iter 290 value 1139.143473
## final value 1139.143279
## converged
## # weights: 61
## initial value 1419395.360927
## iter 10 value 186509.571527
## iter 20 value 7215.249148
## iter 30 value 5607.241744
## iter 40 value 5032.049500
## iter 50 value 4271.261031
## iter 60 value 4088.247275
## iter 70 value 4029.045672
## iter 80 value 3970.924417
## iter 90 value 3856.017163
## iter 100 value 3809.492153
## iter 110 value 3622.428748
## iter 120 value 3057.903696
## iter 130 value 2809.161917
## iter 140 value 2416.262927
## iter 150 value 1820.058290
## iter 160 value 1382.820839
## iter 170 value 1179.226401
## iter 180 value 1115.911817
## iter 190 value 1042.525511
## iter 200 value 940.005258
## iter 210 value 909.066095
## iter 220 value 881.033606
## iter 230 value 872.761878
## iter 240 value 868.823722
## iter 250 value 861.873725
## iter 260 value 855.749084
## iter 270 value 844.754110
## iter 280 value 826.804347
## iter 290 value 816.653710
## iter 300 value 816.018365
## iter 310 value 814.807838
## iter 320 value 812.014330
## iter 330 value 809.141530
## iter 340 value 806.424473
## iter 350 value 800.620058
## iter 360 value 794.993656
## iter 370 value 791.467226
## iter 380 value 786.121669
## iter 390 value 783.201729
## iter 400 value 774.259017
## iter 410 value 766.611068
## iter 420 value 765.104053
## iter 430 value 763.090335
## iter 440 value 753.495200
## iter 450 value 741.318883
## iter 460 value 727.764487
## iter 470 value 717.041868
## iter 480 value 713.670614
## iter 490 value 708.424507
## iter 500 value 703.601768
## final value 703.601768
## stopped after 500 iterations
## # weights: 121
## initial value 1429577.952893
## iter 10 value 2390.312660
## iter 20 value 1072.965842
## iter 30 value 818.206099
## iter 40 value 735.713867
## iter 50 value 699.178663
## iter 60 value 660.249254
## iter 70 value 612.585915
## iter 80 value 574.217161
## iter 90 value 521.422068
## iter 100 value 491.239908
## iter 110 value 473.082935
## iter 120 value 459.193485
## iter 130 value 448.096033
## iter 140 value 435.063752
## iter 150 value 429.396397
## iter 160 value 417.450516
## iter 170 value 402.722272
## iter 180 value 396.715428
## iter 190 value 385.287647
## iter 200 value 375.879126
## iter 210 value 369.548957
## iter 220 value 367.361667
## iter 230 value 366.432644
## iter 240 value 365.834021
## iter 250 value 364.705501
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## iter 270 value 363.964345
## iter 280 value 363.935004
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## iter 300 value 363.810366
## iter 310 value 363.651256
## iter 320 value 363.617918
## iter 330 value 363.599836
## iter 340 value 363.558064
## iter 350 value 363.507149
## iter 360 value 363.500336
## iter 370 value 363.495243
## iter 380 value 363.489160
## iter 390 value 363.474370
## iter 400 value 363.446190
## iter 410 value 363.419429
## iter 420 value 363.394261
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## iter 450 value 363.150435
## iter 460 value 363.086528
## iter 470 value 363.067940
## iter 480 value 363.057312
## iter 490 value 363.031294
## iter 500 value 362.991097
## final value 362.991097
## stopped after 500 iterations
## # weights: 181
## initial value 1350755.748260
## iter 10 value 1220.402385
## iter 20 value 763.100713
## iter 30 value 633.896971
## iter 40 value 466.452598
## iter 50 value 399.771149
## iter 60 value 368.036792
## iter 70 value 329.372157
## iter 80 value 293.813323
## iter 90 value 271.838244
## iter 100 value 254.766132
## iter 110 value 242.285724
## iter 120 value 232.185933
## iter 130 value 216.780280
## iter 140 value 203.960260
## iter 150 value 193.601419
## iter 160 value 187.790115
## iter 170 value 181.652251
## iter 180 value 175.460822
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## iter 200 value 167.889453
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## iter 240 value 135.812465
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## iter 300 value 115.152527
## iter 310 value 112.582094
## iter 320 value 110.277962
## iter 330 value 108.767532
## iter 340 value 106.006354
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## iter 400 value 98.753795
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## iter 460 value 86.046780
## iter 470 value 81.820142
## iter 480 value 76.655161
## iter 490 value 74.604135
## iter 500 value 72.456178
## final value 72.456178
## stopped after 500 iterations
## # weights: 241
## initial value 1362495.416274
## iter 10 value 1158.839226
## iter 20 value 766.560537
## iter 30 value 623.058648
## iter 40 value 514.301309
## iter 50 value 427.288347
## iter 60 value 347.916089
## iter 70 value 307.047745
## iter 80 value 273.553583
## iter 90 value 254.909816
## iter 100 value 227.994250
## iter 110 value 202.027139
## iter 120 value 185.216303
## iter 130 value 171.301744
## iter 140 value 161.649326
## iter 150 value 154.450584
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## iter 170 value 140.130007
## iter 180 value 133.286893
## iter 190 value 124.767430
## iter 200 value 115.677226
## iter 210 value 106.998846
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## iter 260 value 79.060513
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## iter 280 value 73.733815
## iter 290 value 70.893231
## iter 300 value 68.572904
## iter 310 value 66.444971
## iter 320 value 64.926784
## iter 330 value 62.693781
## iter 340 value 60.601489
## iter 350 value 58.470096
## iter 360 value 56.653475
## iter 370 value 54.338856
## iter 380 value 52.034366
## iter 390 value 49.972294
## iter 400 value 47.833071
## iter 410 value 46.014835
## iter 420 value 43.722277
## iter 430 value 41.594130
## iter 440 value 39.594202
## iter 450 value 37.415070
## iter 460 value 35.317576
## iter 470 value 34.456194
## iter 480 value 33.682148
## iter 490 value 33.396856
## iter 500 value 33.290554
## final value 33.290554
## stopped after 500 iterations
## # weights: 25
## initial value 1352804.101603
## iter 10 value 7275.747305
## iter 20 value 5671.697933
## iter 30 value 4972.380138
## iter 40 value 3179.032765
## iter 50 value 1704.377016
## iter 60 value 1450.697286
## iter 70 value 1432.154930
## iter 80 value 1354.504384
## iter 90 value 1336.069149
## iter 100 value 1329.949531
## iter 110 value 1327.451469
## iter 120 value 1327.351137
## iter 130 value 1325.226789
## iter 140 value 1323.396121
## iter 150 value 1322.155002
## iter 160 value 1321.358865
## iter 170 value 1321.326107
## final value 1321.326029
## converged
## # weights: 61
## initial value 1381519.965639
## iter 10 value 156745.089010
## iter 20 value 19165.135566
## iter 30 value 4707.429300
## iter 40 value 2910.576358
## iter 50 value 2507.757296
## iter 60 value 2442.203789
## iter 70 value 2400.313853
## iter 80 value 2392.664782
## iter 90 value 2380.635613
## iter 100 value 2325.955800
## iter 110 value 2199.569931
## iter 120 value 1817.425136
## iter 130 value 1391.646552
## iter 140 value 1254.540213
## iter 150 value 1219.271583
## iter 160 value 1198.840266
## iter 170 value 1174.080006
## iter 180 value 1172.475059
## iter 190 value 1171.034220
## iter 200 value 1170.555707
## iter 210 value 1170.345513
## iter 220 value 1170.212457
## final value 1170.212293
## converged
## # weights: 121
## initial value 1413421.109585
## iter 10 value 1164.292169
## iter 20 value 917.817139
## iter 30 value 794.873953
## iter 40 value 704.864810
## iter 50 value 645.498750
## iter 60 value 599.702107
## iter 70 value 534.997492
## iter 80 value 502.120381
## iter 90 value 474.354445
## iter 100 value 456.554333
## iter 110 value 429.374554
## iter 120 value 393.297491
## iter 130 value 370.217467
## iter 140 value 351.341173
## iter 150 value 339.785544
## iter 160 value 331.455091
## iter 170 value 321.697691
## iter 180 value 310.101631
## iter 190 value 297.704618
## iter 200 value 288.833757
## iter 210 value 279.004290
## iter 220 value 271.420859
## iter 230 value 266.658103
## iter 240 value 261.757831
## iter 250 value 258.717112
## iter 260 value 257.455015
## iter 270 value 256.695570
## iter 280 value 254.898675
## iter 290 value 253.070971
## iter 300 value 251.184672
## iter 310 value 249.060118
## iter 320 value 246.855712
## iter 330 value 244.456969
## iter 340 value 241.698852
## iter 350 value 239.301865
## iter 360 value 236.819552
## iter 370 value 235.800689
## iter 380 value 235.176343
## iter 390 value 234.545566
## iter 400 value 234.206551
## iter 410 value 233.947747
## iter 420 value 233.490559
## iter 430 value 232.665740
## iter 440 value 231.071099
## iter 450 value 229.779929
## iter 460 value 226.675373
## iter 470 value 225.458197
## iter 480 value 224.716210
## iter 490 value 224.045071
## iter 500 value 223.982740
## final value 223.982740
## stopped after 500 iterations
## # weights: 181
## initial value 1401015.855766
## iter 10 value 1199.275633
## iter 20 value 835.197898
## iter 30 value 667.544267
## iter 40 value 529.879974
## iter 50 value 442.998801
## iter 60 value 394.055870
## iter 70 value 339.969879
## iter 80 value 309.182838
## iter 90 value 282.462925
## iter 100 value 261.871797
## iter 110 value 246.757338
## iter 120 value 233.521851
## iter 130 value 225.709182
## iter 140 value 216.937743
## iter 150 value 207.225850
## iter 160 value 196.613876
## iter 170 value 189.384304
## iter 180 value 182.776166
## iter 190 value 174.835674
## iter 200 value 169.414834
## iter 210 value 165.400080
## iter 220 value 162.083955
## iter 230 value 159.058170
## iter 240 value 155.684739
## iter 250 value 150.590213
## iter 260 value 147.163452
## iter 270 value 144.798286
## iter 280 value 141.196879
## iter 290 value 139.373404
## iter 300 value 137.832535
## iter 310 value 136.166298
## iter 320 value 134.235930
## iter 330 value 131.245240
## iter 340 value 124.752549
## iter 350 value 120.818687
## iter 360 value 117.970698
## iter 370 value 116.948100
## iter 380 value 116.534072
## iter 390 value 115.951792
## iter 400 value 115.089880
## iter 410 value 114.442252
## iter 420 value 114.124754
## iter 430 value 113.681684
## iter 440 value 113.234187
## iter 450 value 112.707178
## iter 460 value 112.293287
## iter 470 value 111.870424
## iter 480 value 110.661587
## iter 490 value 107.888475
## iter 500 value 105.202963
## final value 105.202963
## stopped after 500 iterations
## # weights: 241
## initial value 1347790.223486
## iter 10 value 1274.694409
## iter 20 value 808.576311
## iter 30 value 648.638270
## iter 40 value 536.287785
## iter 50 value 385.116096
## iter 60 value 318.603221
## iter 70 value 260.793058
## iter 80 value 221.822935
## iter 90 value 192.716942
## iter 100 value 173.013705
## iter 110 value 155.943760
## iter 120 value 141.480877
## iter 130 value 132.621339
## iter 140 value 125.795299
## iter 150 value 118.299518
## iter 160 value 108.361337
## iter 170 value 99.629122
## iter 180 value 94.671734
## iter 190 value 90.431288
## iter 200 value 87.952221
## iter 210 value 85.894707
## iter 220 value 83.802836
## iter 230 value 81.958632
## iter 240 value 79.765769
## iter 250 value 77.144538
## iter 260 value 74.585932
## iter 270 value 73.050789
## iter 280 value 71.046833
## iter 290 value 69.264372
## iter 300 value 66.930977
## iter 310 value 64.682696
## iter 320 value 62.481003
## iter 330 value 59.675131
## iter 340 value 57.580304
## iter 350 value 55.487808
## iter 360 value 53.851191
## iter 370 value 52.370625
## iter 380 value 50.969494
## iter 390 value 49.219205
## iter 400 value 48.093818
## iter 410 value 47.048995
## iter 420 value 45.992675
## iter 430 value 44.750350
## iter 440 value 43.802829
## iter 450 value 42.887897
## iter 460 value 41.846880
## iter 470 value 41.190365
## iter 480 value 40.540935
## iter 490 value 40.315329
## iter 500 value 40.266483
## final value 40.266483
## stopped after 500 iterations
## # weights: 25
## initial value 1401025.775953
## iter 10 value 11043.404590
## iter 20 value 6905.338699
## iter 30 value 5070.199120
## iter 40 value 3761.324029
## iter 50 value 2309.328186
## iter 60 value 1791.106287
## iter 70 value 1480.559083
## iter 80 value 1386.729777
## iter 90 value 1307.764525
## iter 100 value 1215.603654
## iter 110 value 1178.236964
## iter 120 value 1176.015066
## iter 130 value 1175.123844
## iter 140 value 1172.427184
## final value 1172.395952
## converged
## # weights: 61
## initial value 1404180.903774
## iter 10 value 31573.502817
## iter 20 value 13231.596346
## iter 30 value 7399.407160
## iter 40 value 5159.434897
## iter 50 value 4497.743774
## iter 60 value 3930.768539
## iter 70 value 3124.321637
## iter 80 value 2439.457839
## iter 90 value 2009.434616
## iter 100 value 1573.486539
## iter 110 value 1357.006407
## iter 120 value 1140.361910
## iter 130 value 1019.760235
## iter 140 value 963.360855
## iter 150 value 930.098693
## iter 160 value 908.354357
## iter 170 value 888.272008
## iter 180 value 881.296389
## iter 190 value 876.695459
## iter 200 value 874.615451
## iter 210 value 870.363023
## iter 220 value 838.470595
## iter 230 value 835.049599
## iter 240 value 833.398671
## iter 250 value 831.916336
## iter 260 value 831.420624
## iter 270 value 831.317426
## iter 280 value 831.257805
## final value 831.255576
## converged
## # weights: 121
## initial value 1370782.628932
## iter 10 value 1835.185494
## iter 20 value 1182.742831
## iter 30 value 891.547420
## iter 40 value 816.593527
## iter 50 value 743.507320
## iter 60 value 698.062941
## iter 70 value 671.555344
## iter 80 value 655.315633
## iter 90 value 637.368157
## iter 100 value 621.149274
## iter 110 value 605.459192
## iter 120 value 591.140128
## iter 130 value 582.825726
## iter 140 value 574.736895
## iter 150 value 569.321025
## iter 160 value 565.440523
## iter 170 value 561.799353
## iter 180 value 558.674189
## iter 190 value 557.034366
## iter 200 value 555.502303
## iter 210 value 553.398124
## iter 220 value 551.024506
## iter 230 value 545.203907
## iter 240 value 540.828656
## iter 250 value 539.351522
## iter 260 value 537.898756
## iter 270 value 535.629435
## iter 280 value 533.517504
## iter 290 value 531.924612
## iter 300 value 531.438415
## iter 310 value 531.235964
## iter 320 value 531.033193
## iter 330 value 530.879238
## iter 340 value 530.853299
## iter 350 value 530.849633
## final value 530.849296
## converged
## # weights: 181
## initial value 1396669.835293
## iter 10 value 1459.249337
## iter 20 value 943.847268
## iter 30 value 764.218153
## iter 40 value 688.685007
## iter 50 value 639.392328
## iter 60 value 590.900548
## iter 70 value 547.721395
## iter 80 value 517.740560
## iter 90 value 500.794098
## iter 100 value 490.518404
## iter 110 value 482.730854
## iter 120 value 476.569548
## iter 130 value 468.086292
## iter 140 value 462.251333
## iter 150 value 455.060492
## iter 160 value 449.760458
## iter 170 value 445.160487
## iter 180 value 437.194820
## iter 190 value 432.797278
## iter 200 value 430.241693
## iter 210 value 428.642115
## iter 220 value 426.887464
## iter 230 value 425.414867
## iter 240 value 423.880808
## iter 250 value 423.040186
## iter 260 value 422.280217
## iter 270 value 421.240249
## iter 280 value 420.300563
## iter 290 value 419.523383
## iter 300 value 418.868494
## iter 310 value 418.280813
## iter 320 value 417.845368
## iter 330 value 417.204688
## iter 340 value 416.610410
## iter 350 value 416.282281
## iter 360 value 416.137726
## iter 370 value 416.057895
## iter 380 value 415.878383
## iter 390 value 415.666092
## iter 400 value 415.582978
## iter 410 value 415.530885
## iter 420 value 415.495366
## iter 430 value 415.486320
## iter 440 value 415.483473
## iter 450 value 415.482621
## final value 415.482481
## converged
## # weights: 241
## initial value 1352823.074906
## iter 10 value 1364.247391
## iter 20 value 944.548855
## iter 30 value 767.862014
## iter 40 value 652.839381
## iter 50 value 551.724887
## iter 60 value 499.355724
## iter 70 value 473.834882
## iter 80 value 452.820290
## iter 90 value 430.623398
## iter 100 value 414.948774
## iter 110 value 406.391932
## iter 120 value 398.320012
## iter 130 value 392.456334
## iter 140 value 387.564611
## iter 150 value 384.748525
## iter 160 value 380.359562
## iter 170 value 376.127466
## iter 180 value 372.548326
## iter 190 value 370.233155
## iter 200 value 368.112323
## iter 210 value 365.701135
## iter 220 value 362.817130
## iter 230 value 360.203970
## iter 240 value 357.282967
## iter 250 value 355.076701
## iter 260 value 353.457357
## iter 270 value 352.071116
## iter 280 value 350.532779
## iter 290 value 349.377216
## iter 300 value 348.477559
## iter 310 value 347.176138
## iter 320 value 345.889497
## iter 330 value 344.609580
## iter 340 value 343.327035
## iter 350 value 342.083699
## iter 360 value 340.756455
## iter 370 value 339.689681
## iter 380 value 338.793105
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## iter 400 value 337.526551
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## iter 470 value 334.712651
## iter 480 value 334.293680
## iter 490 value 334.134493
## iter 500 value 334.053183
## final value 334.053183
## stopped after 500 iterations
## # weights: 25
## initial value 1371501.393570
## iter 10 value 15812.800109
## iter 20 value 14498.852704
## iter 30 value 11509.286606
## iter 40 value 5363.631713
## iter 50 value 3885.058771
## iter 60 value 1717.533250
## iter 70 value 1478.854121
## iter 80 value 1390.036701
## iter 90 value 1360.228724
## iter 100 value 1349.258718
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## iter 140 value 1319.265144
## iter 150 value 1239.333849
## iter 160 value 1201.120240
## iter 170 value 1169.830973
## iter 180 value 1165.223702
## iter 190 value 1165.175369
## iter 200 value 1165.173977
## iter 210 value 1165.103978
## iter 220 value 1165.044360
## iter 230 value 1165.033940
## final value 1165.033793
## converged
## # weights: 61
## initial value 1367512.781787
## iter 10 value 5624.713892
## iter 20 value 3454.159861
## iter 30 value 3252.868654
## iter 40 value 3123.240549
## iter 50 value 3070.193976
## iter 60 value 3028.540492
## iter 70 value 2922.773532
## iter 80 value 2758.392306
## iter 90 value 2498.258960
## iter 100 value 2149.398314
## iter 110 value 1417.310793
## iter 120 value 1278.139285
## iter 130 value 1234.033903
## iter 140 value 1217.111818
## iter 150 value 1209.421563
## iter 160 value 1168.270417
## iter 170 value 1069.780277
## iter 180 value 886.425343
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## iter 210 value 869.489018
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## iter 230 value 861.376263
## iter 240 value 860.131048
## iter 250 value 858.754564
## iter 260 value 849.843282
## iter 270 value 730.491024
## iter 280 value 698.628076
## iter 290 value 685.949407
## iter 300 value 683.852151
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## iter 320 value 670.482992
## iter 330 value 662.575406
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## iter 350 value 653.429103
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## iter 390 value 646.601274
## iter 400 value 646.108265
## iter 410 value 646.037710
## iter 420 value 646.008244
## iter 430 value 645.991714
## iter 440 value 645.986599
## final value 645.986347
## converged
## # weights: 121
## initial value 1400728.429412
## iter 10 value 1364.706513
## iter 20 value 874.888205
## iter 30 value 768.707568
## iter 40 value 616.102243
## iter 50 value 540.095203
## iter 60 value 472.917485
## iter 70 value 441.599159
## iter 80 value 416.822087
## iter 90 value 403.795130
## iter 100 value 396.867524
## iter 110 value 388.455958
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## iter 130 value 371.013711
## iter 140 value 365.262408
## iter 150 value 360.761071
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## iter 210 value 336.608525
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## iter 240 value 319.264667
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## iter 260 value 315.580287
## iter 270 value 314.429761
## iter 280 value 313.350681
## iter 290 value 310.104594
## iter 300 value 303.564755
## iter 310 value 296.815682
## iter 320 value 294.366213
## iter 330 value 291.735325
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## iter 350 value 286.346441
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## iter 470 value 270.762054
## iter 480 value 270.516961
## iter 490 value 270.194121
## iter 500 value 270.155559
## final value 270.155559
## stopped after 500 iterations
## # weights: 181
## initial value 1355913.169803
## iter 10 value 1214.270392
## iter 20 value 883.564134
## iter 30 value 700.887629
## iter 40 value 540.335990
## iter 50 value 421.797467
## iter 60 value 369.981514
## iter 70 value 331.587285
## iter 80 value 286.309146
## iter 90 value 245.498765
## iter 100 value 220.977403
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## iter 470 value 120.928311
## iter 480 value 120.271322
## iter 490 value 119.749593
## iter 500 value 119.341751
## final value 119.341751
## stopped after 500 iterations
## # weights: 241
## initial value 1437169.794289
## iter 10 value 1017.666682
## iter 20 value 778.544960
## iter 30 value 656.124445
## iter 40 value 532.577654
## iter 50 value 407.825427
## iter 60 value 333.251616
## iter 70 value 275.905876
## iter 80 value 240.902671
## iter 90 value 221.447792
## iter 100 value 199.164324
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## iter 470 value 60.815296
## iter 480 value 60.207031
## iter 490 value 59.977514
## iter 500 value 59.866257
## final value 59.866257
## stopped after 500 iterations
## # weights: 25
## initial value 1377807.125975
## iter 10 value 6571.488159
## iter 20 value 5474.299869
## iter 30 value 5054.000702
## iter 40 value 4766.021984
## iter 50 value 4441.514471
## iter 60 value 3812.233641
## iter 70 value 1682.001345
## iter 80 value 1352.523134
## iter 90 value 1265.068886
## iter 100 value 1260.123184
## iter 110 value 1239.757906
## iter 120 value 1190.511134
## iter 130 value 1169.493519
## iter 140 value 1166.791414
## iter 150 value 1166.115966
## iter 160 value 1163.250475
## iter 170 value 1112.908266
## iter 180 value 1052.066823
## iter 190 value 959.498259
## iter 200 value 938.049932
## iter 210 value 931.818178
## iter 220 value 929.767684
## iter 230 value 929.584448
## iter 240 value 927.740950
## iter 250 value 926.080303
## iter 260 value 924.047051
## iter 270 value 923.330813
## iter 280 value 923.210748
## iter 290 value 922.871932
## iter 300 value 921.748841
## iter 310 value 921.248068
## iter 320 value 921.034681
## iter 330 value 921.031393
## iter 340 value 921.011617
## iter 350 value 920.986458
## iter 360 value 920.932646
## final value 920.931819
## converged
## # weights: 61
## initial value 1394416.279146
## iter 10 value 17208.579417
## iter 20 value 11420.971312
## iter 30 value 8894.743667
## iter 40 value 3894.354700
## iter 50 value 2457.172741
## iter 60 value 1560.736118
## iter 70 value 1432.108140
## iter 80 value 1327.600256
## iter 90 value 1297.485338
## iter 100 value 1272.102930
## iter 110 value 1206.245478
## iter 120 value 1192.445112
## iter 130 value 1120.609875
## iter 140 value 1111.261865
## iter 150 value 1067.033066
## iter 160 value 1000.138062
## iter 170 value 983.643036
## iter 180 value 967.826068
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## iter 210 value 931.826221
## iter 220 value 926.470247
## iter 230 value 925.423011
## iter 240 value 925.330823
## iter 250 value 921.795980
## iter 260 value 918.060620
## iter 270 value 910.917522
## iter 280 value 875.982628
## iter 290 value 864.935981
## iter 300 value 855.815601
## iter 310 value 847.488670
## iter 320 value 829.575724
## iter 330 value 809.236075
## iter 340 value 798.556319
## iter 350 value 793.713913
## iter 360 value 784.493779
## iter 370 value 755.090620
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## iter 390 value 722.972468
## iter 400 value 710.575390
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## iter 420 value 698.319174
## iter 430 value 698.197091
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## iter 450 value 696.359848
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## iter 470 value 694.256092
## iter 480 value 694.234828
## iter 490 value 693.352075
## iter 500 value 681.434804
## final value 681.434804
## stopped after 500 iterations
## # weights: 121
## initial value 1390362.490798
## iter 10 value 2648.004641
## iter 20 value 1205.686882
## iter 30 value 917.755030
## iter 40 value 800.922374
## iter 50 value 692.218904
## iter 60 value 624.749929
## iter 70 value 588.463821
## iter 80 value 559.316647
## iter 90 value 532.620604
## iter 100 value 481.864839
## iter 110 value 441.655282
## iter 120 value 409.950081
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## iter 140 value 374.135902
## iter 150 value 361.803094
## iter 160 value 343.566365
## iter 170 value 328.360974
## iter 180 value 316.414340
## iter 190 value 310.646083
## iter 200 value 302.806307
## iter 210 value 295.855510
## iter 220 value 291.515606
## iter 230 value 287.724545
## iter 240 value 285.039908
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## iter 280 value 280.775431
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## iter 470 value 259.842159
## iter 480 value 259.215802
## iter 490 value 258.932330
## iter 500 value 258.863038
## final value 258.863038
## stopped after 500 iterations
## # weights: 181
## initial value 1383869.645027
## iter 10 value 1091.540184
## iter 20 value 806.391746
## iter 30 value 684.300192
## iter 40 value 550.975552
## iter 50 value 462.011598
## iter 60 value 409.366577
## iter 70 value 378.826973
## iter 80 value 333.905667
## iter 90 value 305.722793
## iter 100 value 282.804996
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## iter 160 value 195.192175
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## iter 200 value 164.074367
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## iter 460 value 113.811917
## iter 470 value 110.930996
## iter 480 value 108.557149
## iter 490 value 106.887538
## iter 500 value 104.118412
## final value 104.118412
## stopped after 500 iterations
## # weights: 241
## initial value 1291557.121004
## iter 10 value 1821.909269
## iter 20 value 861.958014
## iter 30 value 662.823465
## iter 40 value 544.939462
## iter 50 value 437.716808
## iter 60 value 314.595553
## iter 70 value 273.299689
## iter 80 value 241.417904
## iter 90 value 212.191286
## iter 100 value 191.243296
## iter 110 value 172.740129
## iter 120 value 156.368282
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## iter 160 value 126.962558
## iter 170 value 122.262724
## iter 180 value 116.136330
## iter 190 value 108.954618
## iter 200 value 102.602115
## iter 210 value 96.899967
## iter 220 value 90.876366
## iter 230 value 85.493934
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## iter 280 value 64.242047
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## iter 320 value 57.037132
## iter 330 value 55.713575
## iter 340 value 54.571772
## iter 350 value 53.424373
## iter 360 value 52.135964
## iter 370 value 51.249510
## iter 380 value 50.371189
## iter 390 value 49.141751
## iter 400 value 47.525856
## iter 410 value 46.521561
## iter 420 value 45.506862
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## iter 450 value 43.625907
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## iter 470 value 42.941828
## iter 480 value 42.697766
## iter 490 value 42.587830
## iter 500 value 42.555918
## final value 42.555918
## stopped after 500 iterations
## # weights: 25
## initial value 1372579.696316
## iter 10 value 6620.517409
## iter 20 value 5371.553625
## iter 30 value 4710.588378
## iter 40 value 3381.651345
## iter 50 value 2038.840206
## iter 60 value 1502.700589
## iter 70 value 1398.475042
## iter 80 value 1381.993899
## iter 90 value 1377.585427
## iter 100 value 1343.262786
## iter 110 value 1331.907187
## iter 120 value 1327.214622
## iter 130 value 1324.885694
## iter 140 value 1324.447117
## iter 150 value 1323.962569
## iter 160 value 1322.367410
## iter 170 value 1321.739756
## iter 180 value 1321.181113
## iter 190 value 1321.015005
## iter 200 value 1320.965339
## iter 200 value 1320.965326
## iter 210 value 1320.951379
## final value 1320.950668
## converged
## # weights: 61
## initial value 1409265.016599
## iter 10 value 10053.213138
## iter 20 value 7517.004853
## iter 30 value 5843.041449
## iter 40 value 4020.598466
## iter 50 value 2771.229031
## iter 60 value 2043.457888
## iter 70 value 1480.405356
## iter 80 value 1243.951762
## iter 90 value 1039.848519
## iter 100 value 961.668231
## iter 110 value 916.073889
## iter 120 value 874.119902
## iter 130 value 860.379916
## iter 140 value 857.586689
## iter 150 value 840.522382
## iter 160 value 821.732941
## iter 170 value 812.938170
## iter 180 value 801.320859
## iter 190 value 795.407870
## iter 200 value 789.957027
## iter 210 value 786.922462
## iter 220 value 785.041856
## iter 230 value 782.237408
## iter 240 value 779.379164
## iter 250 value 778.291763
## iter 260 value 778.185551
## iter 270 value 777.770082
## iter 280 value 776.082089
## iter 290 value 773.983796
## iter 300 value 772.877835
## iter 310 value 772.390584
## iter 320 value 772.088415
## iter 330 value 771.796312
## iter 340 value 771.644337
## iter 350 value 771.475921
## iter 360 value 771.091682
## iter 370 value 768.154413
## iter 380 value 767.452754
## iter 390 value 766.548742
## iter 400 value 764.436492
## iter 410 value 759.922245
## iter 420 value 758.867103
## iter 430 value 758.382424
## iter 440 value 757.624797
## iter 450 value 757.410811
## iter 460 value 757.241282
## iter 470 value 757.171615
## iter 480 value 756.993092
## iter 490 value 756.651914
## iter 500 value 756.430855
## final value 756.430855
## stopped after 500 iterations
## # weights: 121
## initial value 1348771.231863
## iter 10 value 2153.978531
## iter 20 value 912.344946
## iter 30 value 757.075887
## iter 40 value 686.727619
## iter 50 value 624.326611
## iter 60 value 565.482013
## iter 70 value 516.615567
## iter 80 value 484.318447
## iter 90 value 460.023250
## iter 100 value 435.507731
## iter 110 value 413.719689
## iter 120 value 403.038519
## iter 130 value 392.183088
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## iter 150 value 378.081087
## iter 160 value 373.071054
## iter 170 value 366.675654
## iter 180 value 354.408421
## iter 190 value 341.185902
## iter 200 value 330.546475
## iter 210 value 322.778823
## iter 220 value 315.541504
## iter 230 value 311.442633
## iter 240 value 307.493502
## iter 250 value 306.600469
## iter 260 value 304.655438
## iter 270 value 301.876211
## iter 280 value 300.319028
## iter 290 value 299.356555
## iter 300 value 295.951787
## iter 310 value 293.427358
## iter 320 value 288.773531
## iter 330 value 278.850399
## iter 340 value 271.921014
## iter 350 value 266.329846
## iter 360 value 264.525485
## iter 370 value 263.862706
## iter 380 value 263.527306
## iter 390 value 263.305211
## iter 400 value 263.229136
## iter 410 value 263.126930
## iter 420 value 262.700327
## iter 430 value 259.663682
## iter 440 value 257.593203
## iter 450 value 256.158937
## iter 460 value 255.067427
## iter 470 value 254.534677
## iter 480 value 254.144963
## iter 490 value 254.047787
## iter 500 value 254.016384
## final value 254.016384
## stopped after 500 iterations
## # weights: 181
## initial value 1430869.219900
## iter 10 value 1203.169298
## iter 20 value 859.977069
## iter 30 value 702.674053
## iter 40 value 569.964810
## iter 50 value 442.915014
## iter 60 value 394.988684
## iter 70 value 359.139436
## iter 80 value 307.774216
## iter 90 value 278.912817
## iter 100 value 260.941975
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## iter 120 value 224.912520
## iter 130 value 210.697153
## iter 140 value 196.916289
## iter 150 value 184.704041
## iter 160 value 173.604627
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## iter 300 value 110.027261
## iter 310 value 107.064712
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## iter 350 value 99.894750
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## iter 390 value 98.223349
## iter 400 value 98.038588
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## iter 460 value 94.254628
## iter 470 value 93.481267
## iter 480 value 93.065227
## iter 490 value 92.681216
## iter 500 value 92.223155
## final value 92.223155
## stopped after 500 iterations
## # weights: 241
## initial value 1447577.885567
## iter 10 value 2744.914996
## iter 20 value 1146.500928
## iter 30 value 710.132165
## iter 40 value 535.028915
## iter 50 value 457.015547
## iter 60 value 364.493274
## iter 70 value 295.639634
## iter 80 value 246.729285
## iter 90 value 218.204345
## iter 100 value 200.386817
## iter 110 value 187.434772
## iter 120 value 179.928689
## iter 130 value 172.975121
## iter 140 value 168.659944
## iter 150 value 164.002296
## iter 160 value 160.826762
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## iter 200 value 141.176566
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## iter 220 value 128.623969
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## iter 250 value 114.418955
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## iter 270 value 108.801299
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## iter 310 value 101.336099
## iter 320 value 99.485886
## iter 330 value 97.426213
## iter 340 value 95.503081
## iter 350 value 93.851554
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## iter 370 value 90.614183
## iter 380 value 88.174462
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## iter 400 value 84.092588
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## iter 470 value 75.197178
## iter 480 value 74.450062
## iter 490 value 74.161486
## iter 500 value 74.124362
## final value 74.124362
## stopped after 500 iterations
## # weights: 25
## initial value 1399249.548941
## iter 10 value 7003.306263
## iter 20 value 5131.185983
## iter 30 value 2857.338188
## iter 40 value 1762.670837
## iter 50 value 1286.671748
## iter 60 value 1246.243551
## iter 70 value 1225.658074
## iter 80 value 1158.227390
## iter 90 value 1143.215915
## iter 100 value 1135.833051
## iter 110 value 1134.240530
## iter 120 value 1133.685525
## iter 130 value 1132.255449
## iter 140 value 1130.797270
## iter 150 value 1129.767567
## iter 160 value 1129.447015
## iter 170 value 1129.435259
## iter 180 value 1129.239655
## iter 190 value 1128.799157
## iter 200 value 1128.619499
## final value 1128.583892
## converged
## # weights: 61
## initial value 1370760.779359
## iter 10 value 19907.146053
## iter 20 value 3239.762629
## iter 30 value 3102.842849
## iter 40 value 3013.132086
## iter 50 value 2948.650387
## iter 60 value 2893.249232
## iter 70 value 2819.007379
## iter 80 value 2782.237329
## iter 90 value 2762.406226
## iter 100 value 2758.731294
## iter 110 value 2755.521585
## iter 120 value 2751.978833
## iter 130 value 2735.855152
## iter 140 value 2733.920928
## iter 150 value 2733.262579
## iter 160 value 2732.679618
## iter 170 value 2731.842361
## iter 180 value 2731.573955
## iter 190 value 2729.314204
## iter 200 value 2713.701059
## iter 210 value 2706.317183
## iter 220 value 2691.657400
## iter 230 value 2689.880493
## iter 240 value 2678.279249
## iter 250 value 2667.300336
## iter 260 value 2666.123031
## iter 270 value 2640.826370
## iter 280 value 2638.156135
## iter 290 value 2628.133612
## iter 300 value 2403.963658
## iter 310 value 2044.160707
## iter 320 value 1475.744751
## iter 330 value 1272.201363
## iter 340 value 1206.136339
## iter 350 value 1194.682270
## iter 360 value 1190.650892
## iter 370 value 1189.620567
## iter 380 value 1189.118859
## iter 390 value 1187.988302
## iter 400 value 1186.085736
## iter 410 value 1186.062944
## final value 1186.062177
## converged
## # weights: 121
## initial value 1351488.804482
## iter 10 value 1274.506092
## iter 20 value 900.715802
## iter 30 value 757.656657
## iter 40 value 644.489324
## iter 50 value 564.932621
## iter 60 value 520.276777
## iter 70 value 471.698120
## iter 80 value 442.779524
## iter 90 value 422.462933
## iter 100 value 408.357824
## iter 110 value 395.506349
## iter 120 value 384.665575
## iter 130 value 376.388211
## iter 140 value 368.343874
## iter 150 value 361.125107
## iter 160 value 354.937938
## iter 170 value 350.923256
## iter 180 value 344.337857
## iter 190 value 337.660117
## iter 200 value 333.167933
## iter 210 value 330.114755
## iter 220 value 327.808591
## iter 230 value 324.523861
## iter 240 value 322.006802
## iter 250 value 320.853066
## iter 260 value 320.224749
## iter 270 value 317.176238
## iter 280 value 313.440666
## iter 290 value 310.918030
## iter 300 value 309.519708
## iter 310 value 307.794084
## iter 320 value 303.841030
## iter 330 value 300.399886
## iter 340 value 299.564688
## iter 350 value 298.166221
## iter 360 value 297.639823
## iter 370 value 297.337774
## iter 380 value 295.457185
## iter 390 value 293.231000
## iter 400 value 292.067611
## iter 410 value 290.788416
## iter 420 value 287.644827
## iter 430 value 285.054687
## iter 440 value 283.389651
## iter 450 value 281.085013
## iter 460 value 278.140069
## iter 470 value 276.125443
## iter 480 value 274.818999
## iter 490 value 273.786823
## iter 500 value 273.615208
## final value 273.615208
## stopped after 500 iterations
## # weights: 181
## initial value 1415817.314266
## iter 10 value 1153.746106
## iter 20 value 832.706463
## iter 30 value 736.038801
## iter 40 value 615.890780
## iter 50 value 494.425847
## iter 60 value 430.101505
## iter 70 value 376.750882
## iter 80 value 328.624886
## iter 90 value 303.685305
## iter 100 value 278.555990
## iter 110 value 251.284476
## iter 120 value 237.195157
## iter 130 value 224.938165
## iter 140 value 215.014507
## iter 150 value 208.263521
## iter 160 value 202.777010
## iter 170 value 198.386358
## iter 180 value 193.232424
## iter 190 value 189.899769
## iter 200 value 186.888340
## iter 210 value 182.727949
## iter 220 value 180.164476
## iter 230 value 176.324788
## iter 240 value 174.429170
## iter 250 value 173.034823
## iter 260 value 172.218962
## iter 270 value 171.557560
## iter 280 value 171.038350
## iter 290 value 170.584040
## iter 300 value 170.214840
## iter 310 value 169.397091
## iter 320 value 168.188245
## iter 330 value 166.969432
## iter 340 value 165.835678
## iter 350 value 164.841096
## iter 360 value 163.915017
## iter 370 value 163.153419
## iter 380 value 162.791262
## iter 390 value 162.174939
## iter 400 value 161.237640
## iter 410 value 160.027661
## iter 420 value 158.675966
## iter 430 value 157.343618
## iter 440 value 155.833902
## iter 450 value 154.797980
## iter 460 value 153.169628
## iter 470 value 150.788225
## iter 480 value 148.332568
## iter 490 value 145.043753
## iter 500 value 142.875852
## final value 142.875852
## stopped after 500 iterations
## # weights: 241
## initial value 1369819.313009
## iter 10 value 1539.309919
## iter 20 value 838.402909
## iter 30 value 728.270365
## iter 40 value 562.696980
## iter 50 value 438.775254
## iter 60 value 372.495928
## iter 70 value 320.728187
## iter 80 value 289.415911
## iter 90 value 248.421982
## iter 100 value 220.815497
## iter 110 value 204.095880
## iter 120 value 186.211479
## iter 130 value 171.728556
## iter 140 value 160.688435
## iter 150 value 153.035179
## iter 160 value 146.617843
## iter 170 value 138.845792
## iter 180 value 133.165401
## iter 190 value 129.035622
## iter 200 value 124.474376
## iter 210 value 119.018202
## iter 220 value 112.296760
## iter 230 value 107.162626
## iter 240 value 103.136083
## iter 250 value 97.478991
## iter 260 value 92.199305
## iter 270 value 88.770772
## iter 280 value 87.430672
## iter 290 value 86.579492
## iter 300 value 86.023614
## iter 310 value 84.610445
## iter 320 value 83.296348
## iter 330 value 82.022265
## iter 340 value 80.599078
## iter 350 value 79.400415
## iter 360 value 78.445209
## iter 370 value 77.183573
## iter 380 value 75.491217
## iter 390 value 72.918922
## iter 400 value 70.337756
## iter 410 value 68.710865
## iter 420 value 66.708595
## iter 430 value 64.351618
## iter 440 value 60.124377
## iter 450 value 55.873978
## iter 460 value 53.803475
## iter 470 value 52.027364
## iter 480 value 50.823363
## iter 490 value 50.311411
## iter 500 value 50.163579
## final value 50.163579
## stopped after 500 iterations
## # weights: 25
## initial value 1400994.033681
## iter 10 value 7955.493644
## iter 20 value 4601.359282
## iter 30 value 3276.361673
## iter 40 value 2016.038266
## iter 50 value 1769.808288
## iter 60 value 1634.133437
## iter 70 value 1395.903713
## iter 80 value 1277.046361
## iter 90 value 1237.554775
## iter 100 value 1220.745836
## iter 110 value 1207.940911
## iter 120 value 1192.950222
## iter 130 value 1190.361158
## final value 1190.338875
## converged
## # weights: 61
## initial value 1421368.223731
## iter 10 value 4612.660994
## iter 20 value 2656.081762
## iter 30 value 2230.571394
## iter 40 value 1952.037648
## iter 50 value 1604.357762
## iter 60 value 1281.314455
## iter 70 value 1155.182669
## iter 80 value 1085.957883
## iter 90 value 1027.889277
## iter 100 value 975.513376
## iter 110 value 945.952700
## iter 120 value 934.648888
## iter 130 value 928.719033
## iter 140 value 924.923107
## iter 150 value 924.209844
## iter 160 value 922.794149
## iter 170 value 919.344881
## iter 180 value 912.969396
## iter 190 value 909.098227
## iter 200 value 904.750679
## iter 210 value 889.186522
## iter 220 value 880.361651
## iter 230 value 878.443738
## iter 240 value 877.397079
## iter 250 value 876.936345
## iter 260 value 874.059015
## iter 270 value 872.065119
## iter 280 value 870.797129
## iter 290 value 868.427374
## iter 300 value 862.199592
## iter 310 value 847.690135
## iter 320 value 839.174099
## iter 330 value 832.954565
## iter 340 value 830.164821
## iter 350 value 829.945493
## iter 360 value 829.915428
## iter 370 value 829.908993
## iter 370 value 829.908988
## iter 370 value 829.908988
## final value 829.908988
## converged
## # weights: 121
## initial value 1415604.581887
## iter 10 value 1227.769186
## iter 20 value 953.745401
## iter 30 value 806.372137
## iter 40 value 732.668901
## iter 50 value 682.436079
## iter 60 value 646.732870
## iter 70 value 630.909842
## iter 80 value 617.691505
## iter 90 value 599.218991
## iter 100 value 583.782858
## iter 110 value 568.961902
## iter 120 value 557.690120
## iter 130 value 549.099975
## iter 140 value 543.626355
## iter 150 value 537.202356
## iter 160 value 533.899409
## iter 170 value 526.474566
## iter 180 value 522.856691
## iter 190 value 519.471537
## iter 200 value 514.946737
## iter 210 value 511.605796
## iter 220 value 506.280424
## iter 230 value 503.509765
## iter 240 value 499.352679
## iter 250 value 496.877813
## iter 260 value 495.376233
## iter 270 value 493.164283
## iter 280 value 492.013969
## iter 290 value 490.904499
## iter 300 value 490.447125
## iter 310 value 490.142662
## iter 320 value 489.946515
## iter 330 value 489.869201
## iter 340 value 489.851547
## iter 350 value 489.849968
## final value 489.849843
## converged
## # weights: 181
## initial value 1355874.647659
## iter 10 value 1268.762649
## iter 20 value 891.091397
## iter 30 value 786.235972
## iter 40 value 681.113258
## iter 50 value 589.561179
## iter 60 value 551.597653
## iter 70 value 524.484523
## iter 80 value 501.372322
## iter 90 value 486.972949
## iter 100 value 476.462205
## iter 110 value 466.885857
## iter 120 value 457.812185
## iter 130 value 449.434337
## iter 140 value 437.278834
## iter 150 value 427.452600
## iter 160 value 417.590269
## iter 170 value 410.122513
## iter 180 value 403.411050
## iter 190 value 398.432436
## iter 200 value 392.773472
## iter 210 value 389.850201
## iter 220 value 387.486318
## iter 230 value 386.011127
## iter 240 value 384.624139
## iter 250 value 381.979273
## iter 260 value 379.263554
## iter 270 value 377.934958
## iter 280 value 376.958222
## iter 290 value 376.446093
## iter 300 value 376.054584
## iter 310 value 375.569120
## iter 320 value 375.023193
## iter 330 value 374.599039
## iter 340 value 374.019086
## iter 350 value 373.631419
## iter 360 value 373.334406
## iter 370 value 373.200845
## iter 380 value 373.078076
## iter 390 value 372.948732
## iter 400 value 372.846201
## iter 410 value 372.814350
## iter 420 value 372.794035
## iter 430 value 372.783049
## iter 440 value 372.780478
## iter 450 value 372.779891
## final value 372.779828
## converged
## # weights: 241
## initial value 1372262.491724
## iter 10 value 1261.420175
## iter 20 value 903.327745
## iter 30 value 786.358543
## iter 40 value 659.204554
## iter 50 value 578.880153
## iter 60 value 539.695219
## iter 70 value 510.418071
## iter 80 value 485.319447
## iter 90 value 462.890066
## iter 100 value 443.788352
## iter 110 value 435.121937
## iter 120 value 429.100086
## iter 130 value 421.543425
## iter 140 value 416.127981
## iter 150 value 411.128164
## iter 160 value 405.615261
## iter 170 value 400.909078
## iter 180 value 397.308013
## iter 190 value 395.236362
## iter 200 value 393.160042
## iter 210 value 390.764108
## iter 220 value 387.820626
## iter 230 value 382.183228
## iter 240 value 371.001090
## iter 250 value 362.754123
## iter 260 value 356.765498
## iter 270 value 351.158632
## iter 280 value 346.726676
## iter 290 value 343.983664
## iter 300 value 341.073193
## iter 310 value 338.323878
## iter 320 value 335.814223
## iter 330 value 333.789370
## iter 340 value 332.110082
## iter 350 value 330.837736
## iter 360 value 329.500651
## iter 370 value 328.821603
## iter 380 value 328.123725
## iter 390 value 327.284490
## iter 400 value 326.824997
## iter 410 value 326.579386
## iter 420 value 326.424029
## iter 430 value 326.262638
## iter 440 value 325.792207
## iter 450 value 324.905723
## iter 460 value 324.172357
## iter 470 value 323.327584
## iter 480 value 322.741780
## iter 490 value 322.509284
## iter 500 value 322.257719
## final value 322.257719
## stopped after 500 iterations
## # weights: 25
## initial value 1369324.548606
## iter 10 value 7029.271043
## iter 20 value 5520.054754
## iter 30 value 3642.486405
## iter 40 value 2619.572918
## iter 50 value 1567.967587
## iter 60 value 1480.984936
## iter 70 value 1461.370256
## iter 80 value 1451.908237
## iter 90 value 1426.602109
## iter 100 value 1417.057151
## iter 110 value 1411.328755
## iter 120 value 1410.344311
## iter 130 value 1409.957226
## iter 140 value 1403.711795
## iter 150 value 1370.224330
## iter 160 value 1355.550457
## iter 170 value 1349.305219
## iter 180 value 1306.933094
## iter 190 value 1288.582534
## iter 200 value 1163.049697
## iter 210 value 992.138398
## iter 220 value 935.697053
## iter 230 value 928.871012
## iter 240 value 928.707657
## iter 250 value 923.356768
## iter 260 value 917.870399
## iter 270 value 916.347507
## iter 280 value 914.823268
## iter 290 value 914.821357
## iter 300 value 914.813899
## iter 310 value 914.791238
## final value 914.790295
## converged
## # weights: 61
## initial value 1403025.240253
## iter 10 value 4728.121653
## iter 20 value 2673.376292
## iter 30 value 1989.686211
## iter 40 value 1598.316078
## iter 50 value 1142.365653
## iter 60 value 938.230331
## iter 70 value 823.159675
## iter 80 value 782.372632
## iter 90 value 751.734381
## iter 100 value 723.270320
## iter 110 value 709.153813
## iter 120 value 690.189915
## iter 130 value 682.817015
## iter 140 value 681.126544
## iter 150 value 676.342571
## iter 160 value 664.105838
## iter 170 value 657.703261
## iter 180 value 653.514365
## iter 190 value 651.402453
## iter 200 value 647.976011
## iter 210 value 646.449817
## iter 220 value 645.405267
## iter 230 value 643.520719
## iter 240 value 641.420334
## iter 250 value 639.915630
## iter 260 value 639.633704
## iter 270 value 639.039992
## iter 280 value 637.250225
## iter 290 value 634.685070
## iter 300 value 629.834822
## iter 310 value 628.067869
## iter 320 value 625.918633
## iter 330 value 620.157621
## iter 340 value 614.206570
## iter 350 value 602.877392
## iter 360 value 590.663175
## iter 370 value 584.956312
## iter 380 value 584.585613
## iter 390 value 584.085472
## iter 400 value 583.271979
## iter 410 value 582.492346
## iter 420 value 581.155864
## iter 430 value 580.221530
## iter 440 value 579.555623
## iter 450 value 579.196360
## iter 460 value 578.968396
## iter 470 value 578.781263
## iter 480 value 578.671547
## iter 490 value 578.628224
## iter 500 value 578.617326
## final value 578.617326
## stopped after 500 iterations
## # weights: 121
## initial value 1320129.751655
## iter 10 value 1625.228776
## iter 20 value 915.604214
## iter 30 value 790.285464
## iter 40 value 683.936799
## iter 50 value 620.961902
## iter 60 value 562.910648
## iter 70 value 521.430401
## iter 80 value 478.618614
## iter 90 value 439.816602
## iter 100 value 419.157181
## iter 110 value 401.197026
## iter 120 value 381.863357
## iter 130 value 363.914179
## iter 140 value 351.182524
## iter 150 value 345.134857
## iter 160 value 338.062706
## iter 170 value 334.163435
## iter 180 value 331.160302
## iter 190 value 328.187079
## iter 200 value 324.922017
## iter 210 value 322.617829
## iter 220 value 321.087773
## iter 230 value 320.451622
## iter 240 value 318.674227
## iter 250 value 314.127727
## iter 260 value 308.242430
## iter 270 value 302.461818
## iter 280 value 298.020699
## iter 290 value 291.990313
## iter 300 value 287.168533
## iter 310 value 284.720694
## iter 320 value 282.705927
## iter 330 value 280.681581
## iter 340 value 275.189209
## iter 350 value 273.266606
## iter 360 value 272.846296
## iter 370 value 272.711421
## iter 380 value 272.588512
## iter 390 value 272.383714
## iter 400 value 271.220670
## iter 410 value 270.770136
## iter 420 value 270.655640
## iter 430 value 270.638546
## iter 440 value 270.634910
## iter 450 value 270.630764
## iter 460 value 270.621230
## iter 470 value 270.557530
## iter 480 value 270.485869
## iter 490 value 270.327169
## iter 500 value 270.219184
## final value 270.219184
## stopped after 500 iterations
## # weights: 181
## initial value 1397857.787408
## iter 10 value 1390.762477
## iter 20 value 826.968495
## iter 30 value 667.515999
## iter 40 value 541.885557
## iter 50 value 439.611594
## iter 60 value 396.945403
## iter 70 value 358.394733
## iter 80 value 297.710310
## iter 90 value 271.522436
## iter 100 value 253.055187
## iter 110 value 239.202558
## iter 120 value 229.915185
## iter 130 value 224.379846
## iter 140 value 219.063936
## iter 150 value 212.612215
## iter 160 value 205.550331
## iter 170 value 196.820685
## iter 180 value 192.324688
## iter 190 value 188.121140
## iter 200 value 184.528932
## iter 210 value 181.329476
## iter 220 value 179.018566
## iter 230 value 177.630247
## iter 240 value 176.020754
## iter 250 value 175.047421
## iter 260 value 173.448251
## iter 270 value 171.180713
## iter 280 value 169.821153
## iter 290 value 168.647747
## iter 300 value 167.921012
## iter 310 value 166.723165
## iter 320 value 165.211617
## iter 330 value 164.230314
## iter 340 value 163.490978
## iter 350 value 163.113115
## iter 360 value 162.773775
## iter 370 value 162.594977
## iter 380 value 162.538072
## iter 390 value 162.448458
## iter 400 value 162.288123
## iter 410 value 162.151497
## iter 420 value 161.819931
## iter 430 value 161.346358
## iter 440 value 160.769040
## iter 450 value 160.349931
## iter 460 value 159.895578
## iter 470 value 159.418228
## iter 480 value 159.097541
## iter 490 value 158.612176
## iter 500 value 158.141032
## final value 158.141032
## stopped after 500 iterations
## # weights: 241
## initial value 1359008.804843
## iter 10 value 1463.672232
## iter 20 value 724.554487
## iter 30 value 567.537212
## iter 40 value 444.004569
## iter 50 value 359.217529
## iter 60 value 305.192641
## iter 70 value 257.194675
## iter 80 value 213.266795
## iter 90 value 176.734721
## iter 100 value 149.220085
## iter 110 value 127.363720
## iter 120 value 113.104715
## iter 130 value 100.390078
## iter 140 value 89.673576
## iter 150 value 84.463974
## iter 160 value 79.801238
## iter 170 value 75.102829
## iter 180 value 70.996846
## iter 190 value 67.144328
## iter 200 value 64.149670
## iter 210 value 61.229742
## iter 220 value 58.877169
## iter 230 value 57.399662
## iter 240 value 55.870285
## iter 250 value 54.161959
## iter 260 value 52.517565
## iter 270 value 50.894570
## iter 280 value 48.717930
## iter 290 value 46.394418
## iter 300 value 43.626692
## iter 310 value 42.066893
## iter 320 value 40.702205
## iter 330 value 39.940052
## iter 340 value 39.075710
## iter 350 value 38.292622
## iter 360 value 37.718801
## iter 370 value 37.356665
## iter 380 value 36.955996
## iter 390 value 36.549113
## iter 400 value 36.255393
## iter 410 value 35.946223
## iter 420 value 35.680790
## iter 430 value 35.394340
## iter 440 value 35.160865
## iter 450 value 34.341863
## iter 460 value 33.614744
## iter 470 value 33.416196
## iter 480 value 33.092091
## iter 490 value 32.927526
## iter 500 value 32.901611
## final value 32.901611
## stopped after 500 iterations
## # weights: 25
## initial value 1417650.248740
## iter 10 value 4079.225355
## iter 20 value 2720.183206
## iter 30 value 2153.001104
## iter 40 value 1266.636488
## iter 50 value 1009.920115
## iter 60 value 968.252913
## iter 70 value 963.521993
## iter 80 value 957.007153
## iter 90 value 933.375612
## iter 100 value 923.142123
## iter 110 value 918.738126
## iter 120 value 918.257227
## iter 130 value 918.190563
## iter 140 value 916.313358
## iter 150 value 915.017274
## iter 160 value 914.738751
## iter 170 value 914.597430
## iter 180 value 914.434302
## iter 190 value 914.075926
## iter 200 value 913.812450
## iter 210 value 913.105557
## iter 220 value 913.067708
## iter 230 value 913.049154
## iter 240 value 912.993866
## iter 250 value 912.961737
## iter 260 value 912.958338
## iter 270 value 912.944660
## iter 280 value 912.574581
## iter 290 value 912.414871
## iter 300 value 912.369872
## iter 310 value 912.368927
## iter 320 value 912.366370
## iter 330 value 912.335954
## iter 340 value 912.196131
## iter 350 value 912.186757
## final value 912.186740
## converged
## # weights: 61
## initial value 1401538.991714
## iter 10 value 3097.422086
## iter 20 value 1321.082665
## iter 30 value 1084.749567
## iter 40 value 928.812640
## iter 50 value 824.877921
## iter 60 value 754.348125
## iter 70 value 700.817491
## iter 80 value 669.033675
## iter 90 value 651.990679
## iter 100 value 644.846596
## iter 110 value 638.290014
## iter 120 value 634.548820
## iter 130 value 632.142067
## iter 140 value 631.320697
## iter 150 value 628.739928
## iter 160 value 622.614609
## iter 170 value 616.196509
## iter 180 value 610.632030
## iter 190 value 603.898426
## iter 200 value 599.312530
## iter 210 value 596.152643
## iter 220 value 594.972528
## iter 230 value 594.783262
## iter 240 value 594.636335
## iter 250 value 594.397304
## iter 260 value 594.360715
## iter 270 value 594.334446
## iter 280 value 594.291013
## iter 290 value 594.201726
## iter 300 value 593.742283
## iter 310 value 592.457381
## iter 320 value 592.233946
## iter 330 value 592.164805
## iter 340 value 591.251286
## iter 350 value 588.740027
## iter 360 value 586.385263
## iter 370 value 584.552889
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## iter 490 value 579.176393
## iter 500 value 579.061910
## final value 579.061910
## stopped after 500 iterations
## # weights: 121
## initial value 1387618.117100
## iter 10 value 2753.266913
## iter 20 value 1898.073403
## iter 30 value 1368.692358
## iter 40 value 1060.779871
## iter 50 value 867.633050
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## final value 325.464283
## stopped after 500 iterations
## # weights: 181
## initial value 1353109.831716
## iter 10 value 1164.641755
## iter 20 value 842.889898
## iter 30 value 654.110766
## iter 40 value 535.081291
## iter 50 value 457.116836
## iter 60 value 400.020065
## iter 70 value 347.795060
## iter 80 value 308.961724
## iter 90 value 284.603092
## iter 100 value 266.385862
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## iter 480 value 125.151554
## iter 490 value 124.254815
## iter 500 value 123.839458
## final value 123.839458
## stopped after 500 iterations
## # weights: 241
## initial value 1405068.188133
## iter 10 value 1595.043138
## iter 20 value 794.538815
## iter 30 value 622.075932
## iter 40 value 508.212107
## iter 50 value 434.460884
## iter 60 value 381.855581
## iter 70 value 340.121663
## iter 80 value 309.685292
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## iter 420 value 60.504387
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## iter 450 value 58.333372
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## iter 470 value 57.739341
## iter 480 value 57.574134
## iter 490 value 57.512981
## iter 500 value 57.491683
## final value 57.491683
## stopped after 500 iterations
## # weights: 25
## initial value 1400163.771020
## iter 10 value 5713.801711
## iter 20 value 5428.728029
## iter 30 value 4766.190000
## iter 40 value 4279.220641
## iter 50 value 3418.831557
## iter 60 value 1868.851241
## iter 70 value 1138.388766
## iter 80 value 973.124748
## iter 90 value 958.627804
## iter 100 value 945.266446
## iter 110 value 935.742619
## iter 120 value 931.612337
## iter 130 value 930.479714
## iter 140 value 929.992672
## iter 150 value 928.878328
## iter 160 value 927.360784
## iter 170 value 926.832065
## iter 180 value 925.766405
## iter 190 value 925.756816
## iter 200 value 925.710542
## iter 210 value 925.253097
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## iter 280 value 924.118729
## iter 290 value 924.116347
## iter 290 value 924.116345
## iter 300 value 924.116080
## iter 300 value 924.116079
## final value 924.115967
## converged
## # weights: 61
## initial value 1385279.120146
## iter 10 value 2381.166495
## iter 20 value 1422.384054
## iter 30 value 1194.124622
## iter 40 value 1024.783772
## iter 50 value 913.654068
## iter 60 value 834.222527
## iter 70 value 778.881039
## iter 80 value 717.896567
## iter 90 value 678.846658
## iter 100 value 661.933289
## iter 110 value 646.737066
## iter 120 value 640.994490
## iter 130 value 638.638933
## iter 140 value 637.807904
## iter 150 value 637.174873
## iter 160 value 636.854474
## iter 170 value 635.755854
## iter 180 value 634.507177
## iter 190 value 633.811073
## iter 200 value 631.931574
## iter 210 value 627.945953
## iter 220 value 622.966155
## iter 230 value 619.847091
## iter 240 value 618.189197
## iter 250 value 617.795579
## iter 260 value 617.536832
## iter 270 value 617.505557
## iter 280 value 617.448387
## iter 290 value 617.297451
## iter 300 value 617.062309
## iter 310 value 616.858225
## iter 320 value 616.746581
## iter 330 value 615.559596
## iter 340 value 614.895280
## iter 350 value 613.692400
## iter 360 value 613.231901
## iter 370 value 613.101282
## iter 380 value 613.032528
## iter 390 value 612.895499
## iter 400 value 612.781296
## iter 410 value 612.717414
## iter 420 value 612.524121
## iter 430 value 612.344326
## iter 440 value 612.329520
## iter 450 value 612.321211
## final value 612.321145
## converged
## # weights: 121
## initial value 1417212.408072
## iter 10 value 5280.796476
## iter 20 value 1688.081874
## iter 30 value 1234.127556
## iter 40 value 1007.940097
## iter 50 value 837.389238
## iter 60 value 754.129475
## iter 70 value 734.818821
## iter 80 value 724.310592
## iter 90 value 694.662279
## iter 100 value 664.287987
## iter 110 value 647.795237
## iter 120 value 628.397121
## iter 130 value 618.382490
## iter 140 value 601.778486
## iter 150 value 588.765063
## iter 160 value 579.641514
## iter 170 value 572.482270
## iter 180 value 566.658088
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## iter 200 value 558.693466
## iter 210 value 551.187800
## iter 220 value 543.161134
## iter 230 value 537.673573
## iter 240 value 533.990321
## iter 250 value 529.678997
## iter 260 value 527.575958
## iter 270 value 526.859297
## iter 280 value 526.778131
## iter 290 value 526.703185
## iter 300 value 526.602259
## iter 310 value 526.313449
## iter 320 value 526.145045
## iter 330 value 525.997876
## iter 340 value 525.811418
## iter 350 value 525.798071
## iter 360 value 525.791518
## iter 370 value 525.778542
## iter 380 value 525.651566
## iter 390 value 522.813974
## iter 400 value 517.316646
## iter 410 value 516.673200
## iter 420 value 516.326686
## iter 430 value 516.172212
## iter 440 value 516.101253
## iter 450 value 516.040796
## iter 460 value 515.868078
## iter 470 value 515.784470
## iter 480 value 514.908834
## iter 490 value 514.074078
## iter 500 value 513.029929
## final value 513.029929
## stopped after 500 iterations
## # weights: 181
## initial value 1389328.202115
## iter 10 value 1244.986765
## iter 20 value 841.651698
## iter 30 value 694.103831
## iter 40 value 539.807267
## iter 50 value 470.187909
## iter 60 value 437.291794
## iter 70 value 409.322621
## iter 80 value 359.162507
## iter 90 value 305.667595
## iter 100 value 272.434827
## iter 110 value 245.040035
## iter 120 value 226.838346
## iter 130 value 214.270839
## iter 140 value 202.473113
## iter 150 value 193.078213
## iter 160 value 187.944295
## iter 170 value 180.063615
## iter 180 value 172.096401
## iter 190 value 164.002801
## iter 200 value 152.796253
## iter 210 value 146.503368
## iter 220 value 142.046155
## iter 230 value 137.918023
## iter 240 value 129.807876
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## iter 260 value 121.593737
## iter 270 value 117.778357
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## iter 300 value 110.921231
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## iter 380 value 100.903903
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## iter 400 value 99.945555
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## iter 460 value 93.966223
## iter 470 value 93.129303
## iter 480 value 92.251014
## iter 490 value 91.061222
## iter 500 value 89.599591
## final value 89.599591
## stopped after 500 iterations
## # weights: 241
## initial value 1404151.633095
## iter 10 value 1215.141017
## iter 20 value 758.976340
## iter 30 value 636.059599
## iter 40 value 539.501681
## iter 50 value 404.964221
## iter 60 value 329.502812
## iter 70 value 282.179854
## iter 80 value 251.846050
## iter 90 value 217.843289
## iter 100 value 190.685503
## iter 110 value 159.314775
## iter 120 value 140.570505
## iter 130 value 129.907216
## iter 140 value 120.152925
## iter 150 value 108.885045
## iter 160 value 100.710033
## iter 170 value 93.269918
## iter 180 value 86.415963
## iter 190 value 80.429147
## iter 200 value 75.051106
## iter 210 value 69.627941
## iter 220 value 64.623830
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## iter 240 value 57.872329
## iter 250 value 54.520209
## iter 260 value 51.124032
## iter 270 value 48.729867
## iter 280 value 47.036047
## iter 290 value 45.766156
## iter 300 value 44.547663
## iter 310 value 42.450448
## iter 320 value 40.365933
## iter 330 value 39.055769
## iter 340 value 37.891417
## iter 350 value 36.772243
## iter 360 value 35.741404
## iter 370 value 34.446509
## iter 380 value 33.192121
## iter 390 value 31.808629
## iter 400 value 30.780333
## iter 410 value 30.060431
## iter 420 value 29.603948
## iter 430 value 29.110078
## iter 440 value 28.337075
## iter 450 value 27.858182
## iter 460 value 27.598151
## iter 470 value 27.283517
## iter 480 value 26.601091
## iter 490 value 26.453481
## iter 500 value 26.416410
## final value 26.416410
## stopped after 500 iterations
## # weights: 25
## initial value 1416989.041823
## iter 10 value 2960.777171
## iter 20 value 1719.661239
## iter 30 value 1410.996786
## iter 40 value 1249.601392
## iter 50 value 1185.450602
## iter 60 value 1174.047284
## iter 70 value 1160.482656
## iter 80 value 1133.990520
## iter 90 value 1042.532070
## iter 100 value 963.746576
## iter 110 value 946.125407
## iter 120 value 939.834232
## iter 130 value 916.539892
## iter 140 value 916.169692
## iter 150 value 915.643496
## iter 160 value 915.563343
## iter 170 value 915.561790
## iter 180 value 915.548558
## iter 190 value 915.479505
## iter 200 value 915.465998
## final value 915.465940
## converged
## # weights: 61
## initial value 1376621.149496
## iter 10 value 16065.394452
## iter 20 value 13781.148611
## iter 30 value 7675.871127
## iter 40 value 4916.573484
## iter 50 value 4012.861697
## iter 60 value 3534.644192
## iter 70 value 3392.922666
## iter 80 value 3372.988033
## iter 90 value 3281.374845
## iter 100 value 2928.796444
## iter 110 value 2719.223246
## iter 120 value 2589.669803
## iter 130 value 2443.890697
## iter 140 value 2111.945079
## iter 150 value 1636.519692
## iter 160 value 1445.032852
## iter 170 value 1338.128519
## iter 180 value 1320.020574
## iter 190 value 1291.274325
## iter 200 value 1232.413561
## iter 210 value 1029.095621
## iter 220 value 971.659463
## iter 230 value 950.468630
## iter 240 value 933.611948
## iter 250 value 914.779687
## iter 260 value 900.079159
## iter 270 value 892.927943
## iter 280 value 888.280447
## iter 290 value 885.154634
## iter 300 value 884.374327
## iter 310 value 883.577239
## iter 320 value 883.563140
## iter 330 value 883.461320
## iter 340 value 882.870279
## iter 350 value 882.840623
## iter 360 value 882.699917
## iter 370 value 881.846974
## iter 380 value 881.669647
## iter 390 value 878.787846
## iter 400 value 877.665008
## iter 410 value 876.869440
## iter 420 value 876.093261
## iter 430 value 875.652578
## iter 440 value 875.408594
## iter 450 value 875.267781
## iter 460 value 874.215600
## iter 470 value 874.067983
## iter 480 value 873.997632
## iter 490 value 873.828343
## iter 500 value 873.827342
## final value 873.827342
## stopped after 500 iterations
## # weights: 121
## initial value 1447298.929750
## iter 10 value 5181.929340
## iter 20 value 1834.352568
## iter 30 value 1225.653568
## iter 40 value 1004.525538
## iter 50 value 780.880576
## iter 60 value 671.151369
## iter 70 value 618.964004
## iter 80 value 585.118213
## iter 90 value 567.470666
## iter 100 value 546.258135
## iter 110 value 521.214113
## iter 120 value 498.475328
## iter 130 value 488.175707
## iter 140 value 484.194106
## iter 150 value 477.805818
## iter 160 value 472.216789
## iter 170 value 466.072367
## iter 180 value 460.034552
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## iter 200 value 454.922184
## iter 210 value 453.047270
## iter 220 value 451.910727
## iter 230 value 451.326885
## iter 240 value 451.028485
## iter 250 value 450.966261
## iter 260 value 450.913724
## iter 270 value 450.797581
## iter 280 value 450.592101
## iter 290 value 450.024132
## iter 300 value 449.604921
## iter 310 value 449.253676
## iter 320 value 447.676271
## iter 330 value 445.942952
## iter 340 value 444.375442
## iter 350 value 443.058276
## iter 360 value 442.127030
## iter 370 value 441.366972
## iter 380 value 440.866502
## iter 390 value 440.656359
## iter 400 value 440.230960
## iter 410 value 439.640559
## iter 420 value 437.857674
## iter 430 value 435.193435
## iter 440 value 431.927080
## iter 450 value 427.592689
## iter 460 value 423.121874
## iter 470 value 418.625128
## iter 480 value 415.019988
## iter 490 value 413.482492
## iter 500 value 413.303608
## final value 413.303608
## stopped after 500 iterations
## # weights: 181
## initial value 1401242.562861
## iter 10 value 1038.030963
## iter 20 value 806.540386
## iter 30 value 653.029115
## iter 40 value 528.121089
## iter 50 value 430.921571
## iter 60 value 378.874183
## iter 70 value 345.569717
## iter 80 value 297.186706
## iter 90 value 246.651319
## iter 100 value 223.122651
## iter 110 value 202.088889
## iter 120 value 190.704742
## iter 130 value 177.292105
## iter 140 value 166.059858
## iter 150 value 153.857644
## iter 160 value 144.155739
## iter 170 value 136.236600
## iter 180 value 130.594237
## iter 190 value 126.433891
## iter 200 value 121.004456
## iter 210 value 118.081121
## iter 220 value 114.939139
## iter 230 value 112.865808
## iter 240 value 110.623610
## iter 250 value 107.852941
## iter 260 value 105.519629
## iter 270 value 102.793834
## iter 280 value 98.091342
## iter 290 value 94.892848
## iter 300 value 93.706192
## iter 310 value 92.467865
## iter 320 value 91.528801
## iter 330 value 90.743983
## iter 340 value 89.936034
## iter 350 value 89.118237
## iter 360 value 88.710885
## iter 370 value 88.563861
## iter 380 value 88.503849
## iter 390 value 88.417194
## iter 400 value 88.335198
## iter 410 value 88.151429
## iter 420 value 87.745279
## iter 430 value 86.792391
## iter 440 value 85.714815
## iter 450 value 84.714625
## iter 460 value 83.814283
## iter 470 value 83.222364
## iter 480 value 82.792683
## iter 490 value 82.264200
## iter 500 value 81.437220
## final value 81.437220
## stopped after 500 iterations
## # weights: 241
## initial value 1383509.087273
## iter 10 value 1553.884628
## iter 20 value 808.961596
## iter 30 value 616.847233
## iter 40 value 519.128414
## iter 50 value 429.490467
## iter 60 value 361.784154
## iter 70 value 311.438305
## iter 80 value 275.671889
## iter 90 value 241.221776
## iter 100 value 206.198722
## iter 110 value 180.662010
## iter 120 value 158.228139
## iter 130 value 141.208696
## iter 140 value 130.266598
## iter 150 value 121.066071
## iter 160 value 115.024752
## iter 170 value 109.791446
## iter 180 value 104.259469
## iter 190 value 98.988901
## iter 200 value 94.028299
## iter 210 value 87.650714
## iter 220 value 83.727167
## iter 230 value 79.972966
## iter 240 value 76.312975
## iter 250 value 73.292122
## iter 260 value 70.790747
## iter 270 value 68.651743
## iter 280 value 66.599493
## iter 290 value 64.516045
## iter 300 value 61.447797
## iter 310 value 58.357381
## iter 320 value 56.272259
## iter 330 value 54.063302
## iter 340 value 51.591521
## iter 350 value 49.956701
## iter 360 value 47.533085
## iter 370 value 44.453532
## iter 380 value 42.270522
## iter 390 value 40.604199
## iter 400 value 39.091190
## iter 410 value 37.786348
## iter 420 value 36.738922
## iter 430 value 35.781893
## iter 440 value 34.683175
## iter 450 value 33.327122
## iter 460 value 32.091603
## iter 470 value 30.910393
## iter 480 value 29.783656
## iter 490 value 29.194707
## iter 500 value 28.986760
## final value 28.986760
## stopped after 500 iterations
## # weights: 25
## initial value 1402566.442260
## iter 10 value 6726.057403
## iter 20 value 5552.715900
## iter 30 value 4708.597370
## iter 40 value 2770.514225
## iter 50 value 1686.500964
## iter 60 value 1470.549296
## iter 70 value 1372.380283
## iter 80 value 1351.850000
## iter 90 value 1306.243563
## iter 100 value 1231.538963
## iter 110 value 1203.274197
## iter 120 value 1201.192130
## iter 130 value 1200.798648
## iter 140 value 1199.616964
## final value 1199.434426
## converged
## # weights: 61
## initial value 1394755.557612
## iter 10 value 8782.134814
## iter 20 value 8243.082055
## iter 30 value 6559.537649
## iter 40 value 4298.018993
## iter 50 value 2835.537276
## iter 60 value 2146.314991
## iter 70 value 1765.966002
## iter 80 value 1601.639379
## iter 90 value 1422.313760
## iter 100 value 1269.018337
## iter 110 value 1192.177176
## iter 120 value 1144.823838
## iter 130 value 1102.857989
## iter 140 value 1057.570147
## iter 150 value 1008.799150
## iter 160 value 975.248580
## iter 170 value 968.210946
## iter 180 value 954.774144
## iter 190 value 937.718569
## iter 200 value 915.595762
## iter 210 value 898.296674
## iter 220 value 885.778288
## iter 230 value 869.773952
## iter 240 value 866.164853
## iter 250 value 861.960647
## iter 260 value 858.232510
## iter 270 value 857.375740
## iter 280 value 856.974669
## iter 290 value 856.915007
## iter 300 value 856.889117
## iter 310 value 856.871027
## iter 320 value 856.857169
## iter 330 value 856.850935
## final value 856.850046
## converged
## # weights: 121
## initial value 1367477.747105
## iter 10 value 1768.419131
## iter 20 value 1203.581706
## iter 30 value 931.875918
## iter 40 value 825.073254
## iter 50 value 770.759876
## iter 60 value 709.900773
## iter 70 value 669.606119
## iter 80 value 642.202656
## iter 90 value 627.880234
## iter 100 value 614.746524
## iter 110 value 599.277602
## iter 120 value 588.394684
## iter 130 value 583.114386
## iter 140 value 578.624098
## iter 150 value 572.087223
## iter 160 value 560.149334
## iter 170 value 552.073114
## iter 180 value 540.965897
## iter 190 value 535.546346
## iter 200 value 532.272992
## iter 210 value 529.138767
## iter 220 value 526.869453
## iter 230 value 525.768212
## iter 240 value 524.709287
## iter 250 value 524.140692
## iter 260 value 523.876788
## iter 270 value 523.183766
## iter 280 value 522.772033
## iter 290 value 522.398290
## iter 300 value 522.188404
## iter 310 value 522.088319
## iter 320 value 522.057748
## iter 330 value 522.053319
## iter 340 value 522.052875
## final value 522.052853
## converged
## # weights: 181
## initial value 1417207.166820
## iter 10 value 1235.252851
## iter 20 value 917.488588
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## iter 480 value 363.323867
## iter 490 value 363.268130
## iter 500 value 363.136861
## final value 363.136861
## stopped after 500 iterations
## # weights: 241
## initial value 1393326.487461
## iter 10 value 1313.639497
## iter 20 value 962.409426
## iter 30 value 794.591473
## iter 40 value 683.057396
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## iter 260 value 365.629887
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## iter 320 value 351.676388
## iter 330 value 348.446662
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## iter 370 value 342.487341
## iter 380 value 341.799617
## iter 390 value 341.248887
## iter 400 value 340.657747
## iter 410 value 340.277499
## iter 420 value 340.029442
## iter 430 value 339.886577
## iter 440 value 339.738245
## iter 450 value 339.585764
## iter 460 value 339.383795
## iter 470 value 339.214297
## iter 480 value 338.835534
## iter 490 value 338.443343
## iter 500 value 338.019234
## final value 338.019234
## stopped after 500 iterations
## # weights: 25
## initial value 1431258.854456
## iter 10 value 6446.527863
## iter 20 value 5131.989858
## iter 30 value 2210.026693
## iter 40 value 1332.912023
## iter 50 value 1141.008505
## iter 60 value 1134.657501
## iter 70 value 1127.787548
## iter 80 value 1092.072780
## iter 90 value 1076.092078
## iter 100 value 1066.317952
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## iter 170 value 1029.728286
## iter 180 value 1021.022758
## iter 190 value 1004.381999
## iter 200 value 998.228276
## iter 210 value 997.006664
## iter 220 value 996.755801
## iter 230 value 996.272145
## iter 240 value 995.895654
## final value 995.880378
## converged
## # weights: 61
## initial value 1362357.540075
## iter 10 value 14388.108612
## iter 20 value 3501.711018
## iter 30 value 2363.539527
## iter 40 value 2008.279237
## iter 50 value 1917.222002
## iter 60 value 1898.000094
## iter 70 value 1885.481407
## iter 80 value 1863.525315
## iter 90 value 1832.443887
## iter 100 value 1823.424649
## iter 110 value 1810.300013
## iter 120 value 1801.385356
## iter 130 value 1791.231213
## iter 140 value 1770.002519
## iter 150 value 1723.330424
## iter 160 value 1704.583613
## iter 170 value 1613.491639
## iter 180 value 1476.279016
## iter 190 value 1315.588124
## iter 200 value 1237.454027
## iter 210 value 1168.856329
## iter 220 value 1085.479279
## iter 230 value 896.533056
## iter 240 value 852.457279
## iter 250 value 843.841810
## iter 260 value 836.731731
## iter 270 value 832.622915
## iter 280 value 829.875713
## iter 290 value 826.180427
## iter 300 value 824.921245
## iter 310 value 824.708015
## iter 320 value 824.617284
## iter 330 value 824.566559
## iter 340 value 824.557856
## iter 350 value 824.554891
## final value 824.554855
## converged
## # weights: 121
## initial value 1400052.966310
## iter 10 value 1358.152484
## iter 20 value 956.669897
## iter 30 value 786.586239
## iter 40 value 669.661695
## iter 50 value 624.628483
## iter 60 value 562.752924
## iter 70 value 520.002211
## iter 80 value 484.749751
## iter 90 value 454.630069
## iter 100 value 437.432488
## iter 110 value 424.946836
## iter 120 value 411.982118
## iter 130 value 390.184380
## iter 140 value 356.703759
## iter 150 value 340.874754
## iter 160 value 327.440394
## iter 170 value 320.388599
## iter 180 value 315.438669
## iter 190 value 310.148328
## iter 200 value 306.089038
## iter 210 value 302.118872
## iter 220 value 299.890431
## iter 230 value 298.899981
## iter 240 value 297.816805
## iter 250 value 297.517901
## iter 260 value 297.394638
## iter 270 value 297.133653
## iter 280 value 296.595771
## iter 290 value 296.218396
## iter 300 value 295.530583
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## iter 320 value 294.553899
## iter 330 value 294.268125
## iter 340 value 293.420659
## iter 350 value 292.639899
## iter 360 value 292.229661
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## iter 450 value 286.438100
## iter 460 value 282.340856
## iter 470 value 279.829645
## iter 480 value 275.825917
## iter 490 value 273.681374
## iter 500 value 270.043666
## final value 270.043666
## stopped after 500 iterations
## # weights: 181
## initial value 1397145.173967
## iter 10 value 1107.342303
## iter 20 value 802.300054
## iter 30 value 622.039376
## iter 40 value 505.018136
## iter 50 value 383.322579
## iter 60 value 321.872358
## iter 70 value 292.057257
## iter 80 value 264.664662
## iter 90 value 244.878589
## iter 100 value 227.397092
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## iter 130 value 191.909072
## iter 140 value 186.580358
## iter 150 value 180.252277
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## iter 200 value 157.194610
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## iter 260 value 139.229020
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## iter 330 value 118.895285
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## iter 470 value 103.332652
## iter 480 value 102.070542
## iter 490 value 100.977154
## iter 500 value 99.897462
## final value 99.897462
## stopped after 500 iterations
## # weights: 241
## initial value 1458997.687732
## iter 10 value 1728.909875
## iter 20 value 886.809325
## iter 30 value 639.532400
## iter 40 value 459.939493
## iter 50 value 369.594574
## iter 60 value 320.892189
## iter 70 value 276.442183
## iter 80 value 226.768651
## iter 90 value 191.459109
## iter 100 value 166.762053
## iter 110 value 143.849439
## iter 120 value 125.852838
## iter 130 value 113.186592
## iter 140 value 103.053678
## iter 150 value 93.675131
## iter 160 value 86.154930
## iter 170 value 81.545408
## iter 180 value 76.731857
## iter 190 value 71.212244
## iter 200 value 64.687899
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## iter 220 value 57.906336
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## iter 270 value 48.197742
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## iter 300 value 44.538232
## iter 310 value 43.750938
## iter 320 value 42.976834
## iter 330 value 42.371725
## iter 340 value 41.697246
## iter 350 value 41.001152
## iter 360 value 40.451780
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## iter 400 value 38.683983
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## iter 420 value 37.680795
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## iter 470 value 35.597777
## iter 480 value 35.314366
## iter 490 value 35.188518
## iter 500 value 35.136736
## final value 35.136736
## stopped after 500 iterations
## # weights: 25
## initial value 1383411.363470
## final value 16085.869356
## converged
## # weights: 61
## initial value 1409907.358667
## iter 10 value 5757.509573
## iter 20 value 2958.999097
## iter 30 value 1862.493172
## iter 40 value 1505.499224
## iter 50 value 1098.572014
## iter 60 value 929.665402
## iter 70 value 880.389936
## iter 80 value 864.287927
## iter 90 value 855.777558
## iter 100 value 848.438593
## iter 110 value 844.104589
## iter 120 value 838.711053
## iter 130 value 830.271948
## iter 140 value 828.759734
## iter 150 value 827.957352
## iter 160 value 825.366560
## iter 170 value 822.013926
## iter 180 value 818.370599
## iter 190 value 816.937676
## iter 200 value 815.836712
## iter 210 value 815.297366
## iter 220 value 814.956971
## iter 230 value 811.241229
## iter 240 value 808.534521
## iter 250 value 807.247274
## iter 260 value 806.748632
## iter 270 value 806.371011
## iter 280 value 806.039333
## iter 290 value 805.762902
## iter 300 value 805.559719
## iter 310 value 805.367140
## iter 320 value 805.333624
## final value 805.332058
## converged
## # weights: 121
## initial value 1405209.960984
## iter 10 value 1509.899575
## iter 20 value 949.955615
## iter 30 value 808.573193
## iter 40 value 686.731264
## iter 50 value 624.805810
## iter 60 value 584.892478
## iter 70 value 532.726886
## iter 80 value 503.125340
## iter 90 value 474.956861
## iter 100 value 437.110137
## iter 110 value 401.536412
## iter 120 value 378.663434
## iter 130 value 361.466298
## iter 140 value 344.630927
## iter 150 value 333.807833
## iter 160 value 319.348199
## iter 170 value 312.944424
## iter 180 value 306.623299
## iter 190 value 299.706138
## iter 200 value 293.699209
## iter 210 value 289.360957
## iter 220 value 287.535018
## iter 230 value 284.388175
## iter 240 value 281.957685
## iter 250 value 280.923003
## iter 260 value 280.565345
## iter 270 value 279.975246
## iter 280 value 279.413753
## iter 290 value 278.674897
## iter 300 value 277.928405
## iter 310 value 277.288360
## iter 320 value 276.534463
## iter 330 value 275.881961
## iter 340 value 275.586819
## iter 350 value 273.936438
## iter 360 value 273.064070
## iter 370 value 272.445777
## iter 380 value 268.725253
## iter 390 value 267.266494
## iter 400 value 265.396027
## iter 410 value 263.608715
## iter 420 value 262.677919
## iter 430 value 261.153969
## iter 440 value 260.160532
## iter 450 value 259.182200
## iter 460 value 258.017731
## iter 470 value 256.090351
## iter 480 value 255.403429
## iter 490 value 255.217935
## iter 500 value 255.211877
## final value 255.211877
## stopped after 500 iterations
## # weights: 181
## initial value 1375284.441241
## iter 10 value 1210.986870
## iter 20 value 837.008587
## iter 30 value 698.629485
## iter 40 value 563.678871
## iter 50 value 453.096069
## iter 60 value 405.928706
## iter 70 value 367.335606
## iter 80 value 328.838616
## iter 90 value 299.945854
## iter 100 value 278.816411
## iter 110 value 255.566263
## iter 120 value 238.708701
## iter 130 value 227.219745
## iter 140 value 215.004770
## iter 150 value 203.260525
## iter 160 value 192.900633
## iter 170 value 184.087919
## iter 180 value 179.166004
## iter 190 value 173.628221
## iter 200 value 170.043679
## iter 210 value 167.653950
## iter 220 value 165.437132
## iter 230 value 162.647261
## iter 240 value 160.793512
## iter 250 value 157.496802
## iter 260 value 156.258937
## iter 270 value 154.741575
## iter 280 value 152.781468
## iter 290 value 150.458933
## iter 300 value 146.926095
## iter 310 value 143.943925
## iter 320 value 141.020713
## iter 330 value 139.172160
## iter 340 value 137.400723
## iter 350 value 135.664353
## iter 360 value 134.235813
## iter 370 value 133.482446
## iter 380 value 133.084559
## iter 390 value 132.576995
## iter 400 value 131.794929
## iter 410 value 130.584196
## iter 420 value 128.695810
## iter 430 value 126.435347
## iter 440 value 125.043602
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## iter 470 value 121.847009
## iter 480 value 120.826781
## iter 490 value 119.777479
## iter 500 value 118.806467
## final value 118.806467
## stopped after 500 iterations
## # weights: 241
## initial value 1406530.155692
## iter 10 value 1439.726198
## iter 20 value 791.240007
## iter 30 value 598.083621
## iter 40 value 498.740921
## iter 50 value 421.953437
## iter 60 value 345.241243
## iter 70 value 303.421895
## iter 80 value 266.099143
## iter 90 value 235.536732
## iter 100 value 208.643190
## iter 110 value 188.380439
## iter 120 value 172.546334
## iter 130 value 159.908528
## iter 140 value 147.661096
## iter 150 value 133.624935
## iter 160 value 119.695338
## iter 170 value 106.861552
## iter 180 value 98.483211
## iter 190 value 92.215894
## iter 200 value 85.938517
## iter 210 value 80.542726
## iter 220 value 73.906730
## iter 230 value 67.683345
## iter 240 value 61.836031
## iter 250 value 55.530542
## iter 260 value 50.399867
## iter 270 value 46.391047
## iter 280 value 43.795048
## iter 290 value 41.819431
## iter 300 value 40.415895
## iter 310 value 38.473289
## iter 320 value 36.540467
## iter 330 value 34.466689
## iter 340 value 32.533051
## iter 350 value 31.132228
## iter 360 value 29.843047
## iter 370 value 28.709029
## iter 380 value 27.496936
## iter 390 value 26.437466
## iter 400 value 25.815869
## iter 410 value 25.247399
## iter 420 value 24.562744
## iter 430 value 24.085237
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## iter 450 value 23.405697
## iter 460 value 22.959267
## iter 470 value 22.556062
## iter 480 value 22.118417
## iter 490 value 21.882705
## iter 500 value 21.790990
## final value 21.790990
## stopped after 500 iterations
## # weights: 25
## initial value 1424522.319439
## iter 10 value 4265.589684
## iter 20 value 2397.472156
## iter 30 value 2129.302172
## iter 40 value 1608.055140
## iter 50 value 1468.965959
## iter 60 value 1404.871251
## iter 70 value 1218.635798
## iter 80 value 1109.838412
## iter 90 value 1049.670285
## iter 100 value 971.956837
## iter 110 value 960.584457
## iter 120 value 942.694225
## iter 130 value 927.482914
## iter 140 value 923.242571
## iter 150 value 921.525523
## iter 160 value 921.440835
## iter 170 value 919.807091
## iter 180 value 918.339304
## iter 190 value 917.247269
## iter 200 value 916.240658
## iter 210 value 916.223135
## iter 220 value 916.148160
## iter 230 value 915.938819
## iter 240 value 915.616802
## iter 250 value 915.071791
## iter 260 value 915.055467
## iter 270 value 914.981239
## iter 280 value 914.856131
## iter 290 value 914.834969
## iter 300 value 914.614932
## iter 310 value 914.549059
## iter 310 value 914.549056
## iter 320 value 914.540817
## iter 330 value 914.526307
## iter 340 value 914.499039
## iter 350 value 914.486810
## iter 360 value 914.333379
## iter 370 value 914.326333
## iter 370 value 914.326329
## final value 914.326197
## converged
## # weights: 61
## initial value 1388300.358808
## iter 10 value 5033.280956
## iter 20 value 2107.840865
## iter 30 value 1510.666890
## iter 40 value 1308.792492
## iter 50 value 1167.764175
## iter 60 value 1055.677025
## iter 70 value 939.585423
## iter 80 value 829.121075
## iter 90 value 761.371244
## iter 100 value 740.853963
## iter 110 value 724.138386
## iter 120 value 698.322843
## iter 130 value 683.876771
## iter 140 value 676.076380
## iter 150 value 669.963127
## iter 160 value 662.146896
## iter 170 value 657.151312
## iter 180 value 649.835304
## iter 190 value 643.244721
## iter 200 value 639.811689
## iter 210 value 637.573599
## iter 220 value 636.185074
## iter 230 value 634.782671
## iter 240 value 631.726444
## iter 250 value 629.611682
## iter 260 value 629.488176
## iter 270 value 629.154273
## iter 280 value 628.560394
## iter 290 value 628.171302
## iter 300 value 628.019045
## iter 310 value 627.847650
## iter 320 value 627.821612
## iter 330 value 627.804271
## iter 340 value 627.761719
## iter 350 value 627.691731
## iter 360 value 627.584353
## iter 370 value 627.454028
## iter 380 value 627.452650
## iter 390 value 627.451976
## iter 400 value 627.451259
## iter 410 value 627.450196
## iter 420 value 627.440710
## iter 430 value 627.422308
## iter 440 value 627.420469
## iter 450 value 627.399582
## iter 460 value 627.398195
## iter 460 value 627.398190
## iter 460 value 627.398190
## final value 627.398190
## converged
## # weights: 121
## initial value 1387699.685308
## iter 10 value 4442.084922
## iter 20 value 1701.262215
## iter 30 value 1099.327612
## iter 40 value 890.022221
## iter 50 value 826.340952
## iter 60 value 762.613543
## iter 70 value 740.161750
## iter 80 value 726.367719
## iter 90 value 722.096916
## iter 100 value 715.322975
## iter 110 value 712.352899
## iter 120 value 709.750440
## iter 130 value 709.358158
## iter 140 value 706.982066
## iter 150 value 702.152951
## iter 160 value 697.395928
## iter 170 value 688.453447
## iter 180 value 681.988238
## iter 190 value 676.272427
## iter 200 value 658.810014
## iter 210 value 645.696516
## iter 220 value 642.904186
## iter 230 value 640.408646
## iter 240 value 637.973011
## iter 250 value 635.218009
## iter 260 value 634.917775
## iter 270 value 633.407233
## iter 280 value 631.186134
## iter 290 value 627.761696
## iter 300 value 626.665449
## iter 310 value 626.046237
## iter 320 value 625.757689
## iter 330 value 625.591667
## iter 340 value 625.471784
## iter 350 value 625.395708
## iter 360 value 625.357167
## iter 370 value 625.314095
## iter 380 value 625.285399
## iter 390 value 625.215867
## iter 400 value 625.200467
## iter 410 value 623.252363
## iter 420 value 617.421335
## iter 430 value 612.639363
## iter 440 value 611.078901
## iter 450 value 609.006449
## iter 460 value 608.418468
## iter 470 value 607.992970
## iter 480 value 607.767551
## iter 490 value 607.585468
## iter 500 value 607.483476
## final value 607.483476
## stopped after 500 iterations
## # weights: 181
## initial value 1304399.167845
## iter 10 value 1141.288055
## iter 20 value 831.013472
## iter 30 value 672.990045
## iter 40 value 550.573160
## iter 50 value 432.802596
## iter 60 value 371.654068
## iter 70 value 324.210511
## iter 80 value 279.891800
## iter 90 value 253.076729
## iter 100 value 233.777589
## iter 110 value 218.600899
## iter 120 value 207.641212
## iter 130 value 198.577162
## iter 140 value 188.303842
## iter 150 value 179.245015
## iter 160 value 174.077584
## iter 170 value 168.610556
## iter 180 value 162.243985
## iter 190 value 155.118536
## iter 200 value 149.944819
## iter 210 value 144.145353
## iter 220 value 137.860236
## iter 230 value 131.067389
## iter 240 value 124.962772
## iter 250 value 120.114394
## iter 260 value 115.821158
## iter 270 value 112.115071
## iter 280 value 109.166425
## iter 290 value 106.931589
## iter 300 value 104.130669
## iter 310 value 101.861303
## iter 320 value 100.814380
## iter 330 value 99.545426
## iter 340 value 98.023952
## iter 350 value 96.433503
## iter 360 value 95.712559
## iter 370 value 95.561295
## iter 380 value 95.496647
## iter 390 value 95.388732
## iter 400 value 95.254251
## iter 410 value 95.128698
## iter 420 value 94.967333
## iter 430 value 94.858961
## iter 440 value 94.675341
## iter 450 value 94.359018
## iter 460 value 93.091568
## iter 470 value 92.117462
## iter 480 value 91.815564
## iter 490 value 91.421153
## iter 500 value 91.081678
## final value 91.081678
## stopped after 500 iterations
## # weights: 241
## initial value 1494792.852562
## iter 10 value 1650.365895
## iter 20 value 881.202073
## iter 30 value 620.063194
## iter 40 value 443.105554
## iter 50 value 352.457176
## iter 60 value 307.924822
## iter 70 value 267.342908
## iter 80 value 240.187042
## iter 90 value 209.420846
## iter 100 value 189.291861
## iter 110 value 172.084488
## iter 120 value 159.109883
## iter 130 value 147.861887
## iter 140 value 138.022472
## iter 150 value 128.825125
## iter 160 value 122.719523
## iter 170 value 117.736931
## iter 180 value 113.139305
## iter 190 value 108.260288
## iter 200 value 102.292462
## iter 210 value 97.274966
## iter 220 value 93.474227
## iter 230 value 88.978878
## iter 240 value 84.267821
## iter 250 value 78.416314
## iter 260 value 73.580207
## iter 270 value 68.086465
## iter 280 value 64.327722
## iter 290 value 61.779981
## iter 300 value 59.467241
## iter 310 value 57.626092
## iter 320 value 55.925421
## iter 330 value 54.086083
## iter 340 value 52.295793
## iter 350 value 50.624234
## iter 360 value 48.828349
## iter 370 value 47.917478
## iter 380 value 47.130496
## iter 390 value 46.414324
## iter 400 value 45.767326
## iter 410 value 44.977099
## iter 420 value 44.021037
## iter 430 value 43.173394
## iter 440 value 41.905073
## iter 450 value 40.429950
## iter 460 value 39.064303
## iter 470 value 38.066126
## iter 480 value 37.133087
## iter 490 value 36.819597
## iter 500 value 36.730349
## final value 36.730349
## stopped after 500 iterations
## # weights: 25
## initial value 1536496.891690
## iter 10 value 21717.180299
## iter 20 value 16517.954314
## iter 30 value 14853.986863
## iter 40 value 9493.319339
## iter 50 value 3352.471783
## iter 60 value 2546.656437
## iter 70 value 2362.381514
## iter 80 value 2144.918770
## iter 90 value 1772.315155
## iter 100 value 1727.300659
## iter 110 value 1603.895751
## iter 120 value 1529.043363
## iter 130 value 1517.249470
## iter 140 value 1517.047736
## iter 140 value 1517.047735
## final value 1517.047735
## converged##################################
# Reporting the apparent results
# for the NN model
##################################
NN_DALEX <- DALEX::explain(NN_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "NN")
(NN_DALEX_Performance <- model_performance(NN_DALEX))## Measures for: regression
## mse : 3.792286
## rmse : 1.947379
## r2 : 0.9387186
## mad : 1.118942
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -6.7019930 -2.2284111 -1.3596353 -0.7496302 -0.2984808 0.0323945 0.3834313
## 70% 80% 90% 100%
## 0.8343248 1.5340057 2.1809442 6.4827220(NN_DALEX_Diagnostics <- model_diagnostics(NN_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :56.39
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.84
## Median : 82.80 Median :4.717 Median :73.53 Median :73.71
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.46
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.31
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :85.01
## residuals abs_residuals label ids
## Min. :-6.7020 Min. :0.006932 Length:292 Min. : 1.00
## 1st Qu.:-0.9466 1st Qu.:0.423443 Class :character 1st Qu.: 73.75
## Median : 0.0324 Median :1.118942 Mode :character Median :146.50
## Mean : 0.0151 Mean :1.437687 Mean :146.50
## 3rd Qu.: 1.2088 3rd Qu.:2.042302 3rd Qu.:219.25
## Max. : 6.4827 Max. :6.701993 Max. :292.00plot(NN_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")
NN_DALEX_VariableImportance <- model_parts(NN_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(NN_DALEX_VariableImportance)
##################################
# Reporting the cross-validation results
# for the NN model
##################################
NN_Tune## Neural Network
##
## 292 samples
## 6 predictor
##
## Pre-processing: centered (4), scaled (4), ignore (2)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## size decay RMSE Rsquared MAE
## 2 0e+00 2.913158 0.8500380 2.299048
## 2 1e-05 3.316750 0.7817528 2.514331
## 2 1e-04 3.312748 0.9255037 2.623476
## 2 1e-03 2.179988 0.9262000 1.649639
## 2 1e-01 2.070060 0.9321827 1.594778
## 5 0e+00 5.259886 0.8372621 2.275294
## 5 1e-05 3.641720 0.8067295 2.076761
## 5 1e-04 2.622448 0.8956273 1.750379
## 5 1e-03 2.288813 0.9148856 1.683198
## 5 1e-01 2.244542 0.9208400 1.660874
## 10 0e+00 4.090892 0.8342633 2.272149
## 10 1e-05 6.251900 0.6529587 3.162672
## 10 1e-04 3.404514 0.8251865 2.140352
## 10 1e-03 2.828581 0.8793067 2.143924
## 10 1e-01 2.323915 0.9157548 1.729951
## 15 0e+00 4.182909 0.7642496 2.918018
## 15 1e-05 4.129136 0.7596288 2.961311
## 15 1e-04 4.024271 0.7862760 2.876569
## 15 1e-03 3.610424 0.8251640 2.696243
## 15 1e-01 2.543247 0.9030396 1.909596
## 20 0e+00 4.804438 0.7226852 3.660531
## 20 1e-05 4.303982 0.7508849 3.181808
## 20 1e-04 4.909855 0.6993867 3.617313
## 20 1e-03 4.837680 0.6951049 3.487130
## 20 1e-01 2.860518 0.8768559 2.040583
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were size = 2 and decay = 0.1.NN_Tune$finalModel## a 10-2-1 network with 25 weights
## inputs: GENDERFemale CONTINAsia CONTINEurope CONTINNorth America CONTINOceania CONTINSouth America INFMOR PERCAP CLTECH NCOMOR
## output(s): .outcome
## options were - linear output units decay=0.1(NN_Tune_RMSE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("RMSE")])## [1] 2.07006(NN_Tune_Rsquared <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("Rsquared")])## [1] 0.9321827(NN_Tune_MAE <- NN_Tune$results[NN_Tune$results$size==NN_Tune$bestTune$size &
NN_Tune$results$decay==NN_Tune$bestTune$decay,
c("MAE")])## [1] 1.5947781.3.6.4 Partial Least Squares Regression (PLS)
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the PLS model
##################################
PLS_Grid = expand.grid(ncomp = 1:5)
##################################
# Running the PLS model
# by setting the caret method to 'pls'
##################################
set.seed(12345678)
PLS_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "pls",
tuneGrid = PLS_Grid,
trControl = KFold_Control)
##################################
# Reporting the apparent results
# for the PLS model
##################################
PLS_DALEX <- DALEX::explain(PLS_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "PLS")
(PLS_DALEX_Performance <- model_performance(PLS_DALEX))## Measures for: regression
## mse : 5.876756
## rmse : 2.424202
## r2 : 0.9050347
## mad : 1.524704
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -7.8171653 -3.1634756 -1.8497887 -1.1383462 -0.5658555 0.2131137 0.6858102
## 70% 80% 90% 100%
## 1.2703500 1.8554207 2.8334884 6.6842346(PLS_DALEX_Diagnostics <- model_diagnostics(PLS_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :57.87
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.38
## Median : 82.80 Median :4.717 Median :73.53 Median :73.39
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.47
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.18
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :88.65
## residuals abs_residuals label ids
## Min. :-7.8172 Min. :0.01345 Length:292 Min. : 1.00
## 1st Qu.:-1.3989 1st Qu.:0.74951 Class :character 1st Qu.: 73.75
## Median : 0.2131 Median :1.52470 Mode :character Median :146.50
## Mean : 0.0000 Mean :1.89719 Mean :146.50
## 3rd Qu.: 1.5808 3rd Qu.:2.60138 3rd Qu.:219.25
## Max. : 6.6842 Max. :7.81717 Max. :292.00plot(PLS_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")
PLS_DALEX_VariableImportance <- model_parts(PLS_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(PLS_DALEX_VariableImportance)
##################################
# Reporting the cross-validation results
# for the PLS model
##################################
PLS_Tune## Partial Least Squares
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## ncomp RMSE Rsquared MAE
## 1 4.997627 0.6039633 4.073632
## 2 3.251276 0.8416354 2.478243
## 3 2.705139 0.8916091 2.067882
## 4 2.572417 0.9014321 2.006329
## 5 2.463222 0.9087387 1.954148
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was ncomp = 5.PLS_Tune$finalModel## Partial least squares regression , fitted with the orthogonal scores algorithm.
## Call:
## plsr(formula = .outcome ~ ., ncomp = ncomp, data = dat, method = "oscorespls")(PLS_Tune_RMSE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("RMSE")])## [1] 2.463222(PLS_Tune_Rsquared <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("Rsquared")])## [1] 0.9087387(PLS_Tune_MAE <- PLS_Tune$results[PLS_Tune$results$ncomp==PLS_Tune$bestTune$ncomp,
c("MAE")])## [1] 1.9541481.3.6.5 Cubist Regression (CUBIST)
Code Chunk | Output
##################################
# Defining the model hyperparameter values
# for the CUBIST model
##################################
CUBIST_Grid = expand.grid(committees = c(10, 20, 30, 40, 50),
neighbors = c(0, 3, 6, 9))
##################################
# Running the CUBIST model
# by setting the caret method to 'cubist'
##################################
set.seed(12345678)
CUBIST_Tune <- train(x = MD.Model.Predictors,
y = MD$LIFEXP,
method = "cubist",
tuneGrid = CUBIST_Grid,
trControl = KFold_Control)
##################################
# Reporting the apparent results
# for the CUBIST model
##################################
CUBIST_DALEX <- DALEX::explain(CUBIST_Tune,
data = MD.Model.Predictors,
y = MD$LIFEXP,
verbose = FALSE,
label = "CUBIST")
(CUBIST_DALEX_Performance <- model_performance(CUBIST_DALEX))## Measures for: regression
## mse : 3.658042
## rmse : 1.912601
## r2 : 0.9408879
## mad : 1.05533
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -6.90777771 -2.15534625 -1.41800017 -0.72510388 -0.33359399 0.01040643
## 60% 70% 80% 90% 100%
## 0.32413965 0.72477280 1.37141615 2.49934372 6.82557834(CUBIST_DALEX_Diagnostics <- model_diagnostics(CUBIST_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :139 Africa :89 Min. :0.3365 Min. :-1.4775
## Female:153 Asia :73 1st Qu.:1.7047 1st Qu.: 0.6183
## Europe :62 Median :2.6100 Median : 1.7960
## North America:31 Mean :2.5569 Mean : 1.7571
## Oceania :18 3rd Qu.:3.5025 3rd Qu.: 2.7938
## South America:19 Max. :4.4864 Max. : 4.7293
## CLTECH NCOMOR y y_hat
## Min. : 0.00 Min. :1.866 Min. :52.84 Min. :55.50
## 1st Qu.: 24.35 1st Qu.:3.944 1st Qu.:66.93 1st Qu.:66.51
## Median : 82.80 Median :4.717 Median :73.53 Median :73.61
## Mean : 64.60 Mean :4.652 Mean :72.47 Mean :72.40
## 3rd Qu.:100.00 3rd Qu.:5.347 3rd Qu.:78.54 3rd Qu.:78.06
## Max. :100.00 Max. :7.959 Max. :87.45 Max. :86.25
## residuals abs_residuals label ids
## Min. :-6.90778 Min. :0.003691 Length:292 Min. : 1.00
## 1st Qu.:-1.06822 1st Qu.:0.447052 Class :character 1st Qu.: 73.75
## Median : 0.01041 Median :1.055330 Mode :character Median :146.50
## Mean : 0.07148 Mean :1.409394 Mean :146.50
## 3rd Qu.: 1.00547 3rd Qu.:2.063695 3rd Qu.:219.25
## Max. : 6.82558 Max. :6.907778 Max. :292.00plot(CUBIST_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")
CUBIST_DALEX_VariableImportance <- model_parts(CUBIST_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(CUBIST_DALEX_VariableImportance)
##################################
# Reporting the cross-validation results
# for the CUBIST model
##################################
CUBIST_Tune## Cubist
##
## 292 samples
## 6 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 264, 263, 263, 262, 263, 264, ...
## Resampling results across tuning parameters:
##
## committees neighbors RMSE Rsquared MAE
## 10 0 2.143307 0.9284329 1.597946
## 10 3 2.161575 0.9264713 1.628394
## 10 6 2.135997 0.9293777 1.600862
## 10 9 2.120955 0.9307087 1.593085
## 20 0 2.118571 0.9293801 1.589678
## 20 3 2.171113 0.9255303 1.629055
## 20 6 2.138896 0.9287024 1.597065
## 20 9 2.118487 0.9303570 1.588802
## 30 0 2.104752 0.9307090 1.577201
## 30 3 2.171787 0.9257896 1.634946
## 30 6 2.135237 0.9293559 1.598399
## 30 9 2.113010 0.9311860 1.587534
## 40 0 2.099914 0.9312085 1.569779
## 40 3 2.164063 0.9263082 1.632470
## 40 6 2.126530 0.9299765 1.592498
## 40 9 2.104461 0.9318479 1.580217
## 50 0 2.096744 0.9316609 1.568414
## 50 3 2.158509 0.9267267 1.629668
## 50 6 2.120160 0.9304991 1.588673
## 50 9 2.097972 0.9324203 1.575005
##
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were committees = 50 and neighbors = 0.CUBIST_Tune$finalModel##
## Call:
## cubist.default(x = x, y = y, committees = param$committees)
##
## Number of samples: 292
## Number of predictors: 6
##
## Number of committees: 50
## Number of rules per committee: 6, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2 ...(CUBIST_Tune_RMSE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("RMSE")])## [1] 2.096744(CUBIST_Tune_Rsquared <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("Rsquared")])## [1] 0.9316609(CUBIST_Tune_MAE <- CUBIST_Tune$results[CUBIST_Tune$results$committees==CUBIST_Tune$bestTune$committees &
CUBIST_Tune$results$neighbors==CUBIST_Tune$bestTune$neighbors,
c("MAE")])## [1] 1.5684141.3.7 Model Performance Validation
Code Chunk | Output
##################################
# Evaluating the models
# on the model test data
##################################
##################################
# Formulating the DALEX object
# for the Best GBM model
# as applied to the model test data
##################################
GBM_DALEX <- DALEX::explain(GBM_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "GBM")
(GBM_DALEX_Performance <- model_performance(GBM_DALEX))## Measures for: regression
## mse : 4.877673
## rmse : 2.208545
## r2 : 0.9084603
## mad : 1.155262
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -4.35206346 -2.32130175 -1.24214793 -0.82877725 -0.46233520 0.06134295
## 60% 70% 80% 90% 100%
## 0.59630498 0.73104236 1.75812056 3.45145715 6.41897554(GBM_DALEX_Diagnostics <- model_diagnostics(GBM_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :55.11
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:68.18
## Median : 90.20 Median :4.742 Median :73.51 Median :73.02
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.33
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:78.53
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.82
## residuals abs_residuals label ids
## Min. :-4.35206 Min. :0.003661 Length:72 Min. : 1.00
## 1st Qu.:-1.04139 1st Qu.:0.609636 Class :character 1st Qu.:18.75
## Median : 0.06134 Median :1.155262 Mode :character Median :36.50
## Mean : 0.27559 Mean :1.661957 Mean :36.50
## 3rd Qu.: 1.45652 3rd Qu.:2.352673 3rd Qu.:54.25
## Max. : 6.41898 Max. :6.418976 Max. :72.00plot(GBM_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("GBM: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best RF model
# as applied to the model test data
##################################
RF_DALEX <- DALEX::explain(RF_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "RF")
(RF_DALEX_Performance <- model_performance(RF_DALEX))## Measures for: regression
## mse : 6.25631
## rmse : 2.501262
## r2 : 0.8825872
## mad : 1.594092
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -7.2080243 -3.0176466 -2.1047296 -1.2247647 -0.2712163 0.1992440 0.7128073
## 70% 80% 90% 100%
## 1.1067986 1.7677385 3.1744799 7.4973476(RF_DALEX_Diagnostics <- model_diagnostics(RF_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :54.85
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:68.50
## Median : 90.20 Median :4.742 Median :73.51 Median :73.19
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.58
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:78.61
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.60
## residuals abs_residuals label ids
## Min. :-7.20802 Min. :0.01395 Length:72 Min. : 1.00
## 1st Qu.:-1.69056 1st Qu.:0.73760 Class :character 1st Qu.:18.75
## Median : 0.19924 Median :1.59409 Mode :character Median :36.50
## Mean : 0.02669 Mean :1.91950 Mean :36.50
## 3rd Qu.: 1.50013 3rd Qu.:2.74804 3rd Qu.:54.25
## Max. : 7.49735 Max. :7.49735 Max. :72.00plot(RF_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("RF: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best NN model
# as applied to the model test data
##################################
NN_DALEX <- DALEX::explain(NN_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "NN")
(NN_DALEX_Performance <- model_performance(NN_DALEX))## Measures for: regression
## mse : 5.300062
## rmse : 2.302186
## r2 : 0.9005332
## mad : 1.293056
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -6.0612264 -2.8063521 -1.3495125 -1.0121289 -0.3547678 0.2159281 0.6061218
## 70% 80% 90% 100%
## 1.1206919 1.8356939 2.8346540 7.8217567(NN_DALEX_Diagnostics <- model_diagnostics(NN_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :52.40
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:66.97
## Median : 90.20 Median :4.742 Median :73.51 Median :73.22
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.48
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:78.83
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.49
## residuals abs_residuals label ids
## Min. :-6.0612 Min. :0.06061 Length:72 Min. : 1.00
## 1st Qu.:-1.2126 1st Qu.:0.72930 Class :character 1st Qu.:18.75
## Median : 0.2159 Median :1.29306 Mode :character Median :36.50
## Mean : 0.1293 Mean :1.77153 Mean :36.50
## 3rd Qu.: 1.5601 3rd Qu.:2.67075 3rd Qu.:54.25
## Max. : 7.8218 Max. :7.82176 Max. :72.00plot(NN_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("NN: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best PLS model
# as applied to the model test data
##################################
PLS_DALEX <- DALEX::explain(PLS_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "PLS")
(PLS_DALEX_Performance <- model_performance(PLS_DALEX))## Measures for: regression
## mse : 7.220162
## rmse : 2.687036
## r2 : 0.8644985
## mad : 1.875765
##
## Residuals:
## 0% 10% 20% 30% 40% 50% 60%
## -6.7868694 -2.5089071 -1.8150301 -1.1801931 -0.4920039 0.1375484 0.7844976
## 70% 80% 90% 100%
## 2.0494752 2.6262401 3.8453344 6.0828174(PLS_DALEX_Diagnostics <- model_diagnostics(PLS_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :57.14
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:67.18
## Median : 90.20 Median :4.742 Median :73.51 Median :72.99
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.33
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:77.39
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.75
## residuals abs_residuals label ids
## Min. :-6.7869 Min. :0.04887 Length:72 Min. : 1.00
## 1st Qu.:-1.4065 1st Qu.:0.82974 Class :character 1st Qu.:18.75
## Median : 0.1375 Median :1.87576 Mode :character Median :36.50
## Mean : 0.2816 Mean :2.15539 Mean :36.50
## 3rd Qu.: 2.4014 3rd Qu.:3.14434 3rd Qu.:54.25
## Max. : 6.0828 Max. :6.78687 Max. :72.00plot(PLS_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("PLS: Observed and Predicted LIFEXP")
##################################
# Formulating the DALEX object
# for the Best CUBIST model
# as applied to the model test data
##################################
CUBIST_DALEX <- DALEX::explain(CUBIST_Tune,
data = MT.Model.Predictors,
y = MT$LIFEXP,
verbose = FALSE,
label = "CUBIST")
(CUBIST_DALEX_Performance <- model_performance(CUBIST_DALEX))## Measures for: regression
## mse : 4.955851
## rmse : 2.226174
## r2 : 0.9069931
## mad : 1.555514
##
## Residuals:
## 0% 10% 20% 30% 40% 50%
## -6.27483923 -2.34803998 -1.75093724 -1.08073022 -0.25740164 0.08468332
## 60% 70% 80% 90% 100%
## 0.74473043 1.16073038 1.70618640 2.84412369 5.15525385(CUBIST_DALEX_Diagnostics <- model_diagnostics(CUBIST_DALEX))## GENDER CONTIN INFMOR PERCAP
## Male :43 Africa :17 Min. :0.6419 Min. :-1.4775
## Female:29 Asia :21 1st Qu.:1.7090 1st Qu.: 0.7926
## Europe :16 Median :2.6602 Median : 1.7266
## North America:11 Mean :2.5559 Mean : 1.7439
## Oceania : 2 3rd Qu.:3.4135 3rd Qu.: 2.8314
## South America: 5 Max. :4.3858 Max. : 4.4466
## CLTECH NCOMOR y y_hat
## Min. : 0.20 Min. :2.511 Min. :51.20 Min. :55.56
## 1st Qu.: 49.02 1st Qu.:3.887 1st Qu.:67.42 1st Qu.:67.93
## Median : 90.20 Median :4.742 Median :73.51 Median :72.67
## Mean : 72.08 Mean :4.752 Mean :72.61 Mean :72.49
## 3rd Qu.:100.00 3rd Qu.:5.631 3rd Qu.:78.47 3rd Qu.:77.88
## Max. :100.00 Max. :7.406 Max. :86.20 Max. :84.71
## residuals abs_residuals label ids
## Min. :-6.27484 Min. :0.01256 Length:72 Min. : 1.00
## 1st Qu.:-1.54890 1st Qu.:0.71120 Class :character 1st Qu.:18.75
## Median : 0.08468 Median :1.55551 Mode :character Median :36.50
## Mean : 0.11581 Mean :1.73586 Mean :36.50
## 3rd Qu.: 1.40404 3rd Qu.:2.38064 3rd Qu.:54.25
## Max. : 5.15525 Max. :6.27484 Max. :72.00plot(CUBIST_DALEX_Diagnostics,
variable = "y",
yvariable = "y_hat") +
geom_point(size=3) +
scale_x_continuous("Observed LIFEXP") +
scale_y_continuous("Predicted LIFEXP") +
geom_abline(slope = 1) +
ggtitle("CUBIST: Observed and Predicted LIFEXP")
1.3.8 Model Selection
Code Chunk | Output
##################################
# Consolidating the performance
# on the model test data
##################################
plot(GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance)
plot(GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance,
geom = "boxplot")
plot(GBM_DALEX_Performance,
RF_DALEX_Performance,
NN_DALEX_Performance,
PLS_DALEX_Performance,
CUBIST_DALEX_Performance,
geom = "histogram")
##################################
# Consolidating the variable importance
# on the model test data
##################################
GBM_DALEX_VariableImportance <- model_parts(GBM_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
RF_DALEX_VariableImportance <- model_parts(RF_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
NN_DALEX_VariableImportance <- model_parts(NN_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
PLS_DALEX_VariableImportance <- model_parts(PLS_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
CUBIST_DALEX_VariableImportance <- model_parts(CUBIST_DALEX,
loss_function = loss_root_mean_square,
B = 200,
N = NULL)
plot(GBM_DALEX_VariableImportance,
RF_DALEX_VariableImportance,
NN_DALEX_VariableImportance,
PLS_DALEX_VariableImportance,
CUBIST_DALEX_VariableImportance)
1.3.9 Model Post-Hoc Analysis
1.3.9.1 Dataset Level Exploration : Variable Importance (DLE_VARIMP)
Code Chunk | Output
##################################
# Summarizing the variable importance
# for the final model - GBM
##################################
GBM_DALEX_VariableImportance## variable mean_dropout_loss label
## 1 _full_model_ 2.208545 GBM
## 2 PERCAP 2.373317 GBM
## 3 CLTECH 2.479101 GBM
## 4 GENDER 2.625180 GBM
## 5 CONTIN 2.633023 GBM
## 6 NCOMOR 3.995838 GBM
## 7 INFMOR 7.080997 GBM
## 8 _baseline_ 10.054004 GBMplot(GBM_DALEX_VariableImportance)
1.3.9.2 Dataset Level Exploration : Partial Dependence Plots (DLE_PDP)
Code Chunk | Output
##################################
# Formulating the partial dependence plots
# for the final model - GBM
# using the numeric variables
##################################
GBM_DALEX_PartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR")
GBM_DALEX_PartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR")
GBM_DALEX_PartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH")
GBM_DALEX_PartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP")
(GBM_DALEX_PDP_INFMOR <- plot(GBM_DALEX_PartialDependencePlot_INFMOR,
geom = "profiles"))
(GBM_DALEX_PDP_NCOMOR <- plot(GBM_DALEX_PartialDependencePlot_NCOMOR,
geom = "profiles"))
(GBM_DALEX_PDP_CLTECH <- plot(GBM_DALEX_PartialDependencePlot_CLTECH,
geom = "profiles"))
(GBM_DALEX_PDP_PERCAP <- plot(GBM_DALEX_PartialDependencePlot_PERCAP,
geom = "profiles"))
##################################
# Formulating the grouped partial dependence plots
# for the final model - GBM
# using the numeric variables
# stratified by GENDER
##################################
GBM_DALEX_GroupedPartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH",
groups = "GENDER")
GBM_DALEX_GroupedPartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP",
groups = "GENDER")
(GBM_DALEX_GPDP_INFMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_INFMOR,
geom = "profiles"))
(GBM_DALEX_GPDP_NCOMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_NCOMOR,
geom = "profiles"))
(GBM_DALEX_GPDP_CLTECH <- plot(GBM_DALEX_GroupedPartialDependencePlot_CLTECH,
geom = "profiles"))
(GBM_DALEX_GPDP_PERCAP <- plot(GBM_DALEX_GroupedPartialDependencePlot_PERCAP,
geom = "profiles"))
##################################
# Formulating the grouped partial dependence plots
# for the final model - GBM
# using the numeric variables
# stratified by CONTIN
##################################
GBM_DALEX_GroupedPartialDependencePlot_INFMOR <- model_profile(GBM_DALEX,
variables = "INFMOR",
groups = "CONTIN")
GBM_DALEX_GroupedPartialDependencePlot_NCOMOR <- model_profile(GBM_DALEX,
variables = "NCOMOR",
groups = "CONTIN")
GBM_DALEX_GroupedPartialDependencePlot_CLTECH <- model_profile(GBM_DALEX,
variables = "CLTECH",
groups = "CONTIN")
GBM_DALEX_GroupedPartialDependencePlot_PERCAP <- model_profile(GBM_DALEX,
variables = "PERCAP",
groups = "CONTIN")
(GBM_DALEX_GPDP_INFMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_INFMOR,
geom = "profiles"))
(GBM_DALEX_GPDP_NCOMOR <- plot(GBM_DALEX_GroupedPartialDependencePlot_NCOMOR,
geom = "profiles"))
(GBM_DALEX_GPDP_CLTECH <- plot(GBM_DALEX_GroupedPartialDependencePlot_CLTECH,
geom = "profiles"))
(GBM_DALEX_GPDP_PERCAP <- plot(GBM_DALEX_GroupedPartialDependencePlot_PERCAP,
geom = "profiles"))
##################################
# Formulating the partial dependence plots
# for the final model - GBM
# using the factor variables
##################################
GBM_DALEX_PartialDependencePlot_GENDER <- model_profile(GBM_DALEX,
variable_type = 'categorical',
variables = "GENDER")
GBM_DALEX_PartialDependencePlot_CONTIN <- model_profile(GBM_DALEX,
variable_type = 'categorical',
variables = "CONTIN")
(GBM_DALEX_PDP_GENDER <- plot(GBM_DALEX_PartialDependencePlot_GENDER,
geom = "profiles"))
(GBM_DALEX_PDP_CONTIN <- plot(GBM_DALEX_PartialDependencePlot_CONTIN,
geom = "profiles"))
1.3.9.3 Instance Level Exploration : Breakdown Plots (ILE_BP)
Code Chunk | Output
##################################
# Formulating the sampled instances
# for illustration
##################################
(Instance_1_Philippines_Female <- PME[PME$COUNTRY=="Philippines" & PME$GENDER=="Female",
c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")])## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR LIFEXP
## 141 Female Asia 2.944439 1.248566 47.4 4.704261 75.505(Instance_2_Philippines_Male <- PME[PME$COUNTRY=="Philippines" & PME$GENDER=="Male",
c("GENDER","CONTIN","INFMOR","PERCAP","CLTECH","NCOMOR","LIFEXP")])## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR LIFEXP
## 338 Male Asia 3.173878 1.248566 47.4 5.950788 67.263##################################
# Obtaining the breakdown plots
# for the individual instances
##################################
(Instance_1_GBM_BDP <- DALEX::predict_parts(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
type = "break_down"))## contribution
## GBM: intercept 72.334
## GBM: GENDER = Female 1.285
## GBM: CONTIN = Asia 0.525
## GBM: INFMOR = 2.944 0.695
## GBM: NCOMOR = 4.704 -0.161
## GBM: PERCAP = 1.249 -0.111
## GBM: CLTECH = 47.4 0.014
## GBM: prediction 74.581plot(Instance_1_GBM_BDP)
(Instance_2_GBM_BDP <- DALEX::predict_parts(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
type = "break_down"))## contribution
## GBM: intercept 72.334
## GBM: NCOMOR = 5.951 -3.063
## GBM: INFMOR = 3.174 -0.819
## GBM: GENDER = Male -0.926
## GBM: CONTIN = Asia 0.813
## GBM: PERCAP = 1.249 0.256
## GBM: CLTECH = 47.4 -0.142
## GBM: prediction 68.452plot(Instance_2_GBM_BDP)
1.3.9.4 Instance Level Exploration : Shapley Additive Explanations (ILE_SHAP)
Code Chunk | Output
##################################
# Obtaining the shapley additive explanations
# for the individual instances
##################################
(Instance_1_GBM_SHAP <- DALEX::predict_parts(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
type = "shap",
B = 25))## min q1 median mean q3
## GBM: CLTECH = 47.4 -0.1096761 0.01141644 0.0425315 0.04627025 0.11125913
## GBM: CONTIN = Asia 0.1997490 0.27790908 0.4226572 0.42047277 0.55714718
## GBM: GENDER = Female 1.0250692 1.17867184 1.2296506 1.22132808 1.28532575
## GBM: INFMOR = 2.944 0.5616631 0.60727978 1.2181237 1.12691237 1.49359879
## GBM: NCOMOR = 4.704 -0.9672737 -0.69107878 -0.5245096 -0.47182445 -0.17498794
## GBM: PERCAP = 1.249 -0.3942109 -0.20796143 -0.1052200 -0.09626333 -0.08089423
## max
## GBM: CLTECH = 47.4 0.1437923
## GBM: CONTIN = Asia 0.6872704
## GBM: GENDER = Female 1.3923546
## GBM: INFMOR = 2.944 1.6130390
## GBM: NCOMOR = 4.704 0.0998502
## GBM: PERCAP = 1.249 0.1876230plot(Instance_1_GBM_SHAP)
(Instance_2_GBM_SHAP <- DALEX::predict_parts(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
type = "shap",
B = 25))## min q1 median mean q3
## GBM: CLTECH = 47.4 -0.1833863 -0.13902978 -0.10967607 -0.08443553 -0.03754566
## GBM: CONTIN = Asia 0.4248417 0.61800333 0.62294753 0.63130944 0.66081998
## GBM: GENDER = Male -1.0374690 -0.89214274 -0.80993627 -0.79832321 -0.71019714
## GBM: INFMOR = 3.174 -2.0087792 -1.95450059 -1.74699387 -1.44666001 -0.81061732
## GBM: NCOMOR = 5.951 -3.1802712 -2.89954326 -1.90426390 -2.23039094 -1.80864334
## GBM: PERCAP = 1.249 -0.2740559 -0.08089423 -0.01069142 0.04676586 0.24048722
## max
## GBM: CLTECH = 47.4 0.06412474
## GBM: CONTIN = Asia 0.90402611
## GBM: GENDER = Male -0.61636661
## GBM: INFMOR = 3.174 -0.48666306
## GBM: NCOMOR = 5.951 -1.46373246
## GBM: PERCAP = 1.249 0.26382681plot(Instance_2_GBM_SHAP)
1.3.9.5 Instance Level Exploration : Ceteris Paribus Profiles (ILE_CPP)
Code Chunk | Output
##################################
# Obtaining the ceteris paribus profiles
# for the individual instances
##################################
(Instance_1_GBM_CPP <- DALEX::predict_profile(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)]))## Top profiles :
## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR _yhat_ _vname_
## 141 Male Asia 2.944439 1.248566 47.4 4.704261 72.52199 GENDER
## 141.1 Female Asia 2.944439 1.248566 47.4 4.704261 74.58094 GENDER
## 1411 Female Africa 2.944439 1.248566 47.4 4.704261 73.53995 CONTIN
## 141.110 Female Asia 2.944439 1.248566 47.4 4.704261 74.58094 CONTIN
## 141.2 Female Europe 2.944439 1.248566 47.4 4.704261 74.92891 CONTIN
## 141.3 Female North America 2.944439 1.248566 47.4 4.704261 74.52190 CONTIN
## _ids_ _label_
## 141 141 GBM
## 141.1 141 GBM
## 1411 141 GBM
## 141.110 141 GBM
## 141.2 141 GBM
## 141.3 141 GBM
##
##
## Top observations:
## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR _yhat_ _label_ _ids_
## 141 Female Asia 2.944439 1.248566 47.4 4.704261 74.58094 GBM 1plot(Instance_1_GBM_CPP,
variables = c("INFMOR","PERCAP","CLTECH","NCOMOR")) +
ggtitle("Ceteris-paribus profile", "") +
ylim(55, 80)
plot(Instance_1_GBM_CPP,
variables = c("GENDER","CONTIN"),
variable_type = "categorical",
categorical_type = "bars") +
ggtitle("Ceteris-paribus profile", "")
(Instance_2_GBM_CPP <- DALEX::predict_profile(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)]))## Top profiles :
## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR _yhat_ _vname_
## 338 Male Asia 3.173878 1.248566 47.4 5.950788 68.45231 GENDER
## 338.1 Female Asia 3.173878 1.248566 47.4 5.950788 70.66729 GENDER
## 3381 Male Africa 3.173878 1.248566 47.4 5.950788 66.21920 CONTIN
## 338.110 Male Asia 3.173878 1.248566 47.4 5.950788 68.45231 CONTIN
## 338.2 Male Europe 3.173878 1.248566 47.4 5.950788 68.11751 CONTIN
## 338.3 Male North America 3.173878 1.248566 47.4 5.950788 68.25643 CONTIN
## _ids_ _label_
## 338 338 GBM
## 338.1 338 GBM
## 3381 338 GBM
## 338.110 338 GBM
## 338.2 338 GBM
## 338.3 338 GBM
##
##
## Top observations:
## GENDER CONTIN INFMOR PERCAP CLTECH NCOMOR _yhat_ _label_ _ids_
## 338 Male Asia 3.173878 1.248566 47.4 5.950788 68.45231 GBM 1plot(Instance_2_GBM_CPP,
variables = c("INFMOR","PERCAP","CLTECH","NCOMOR")) +
ggtitle("Ceteris-paribus profile", "") +
ylim(55, 80)
plot(Instance_2_GBM_CPP,
variables = c("GENDER","CONTIN"),
variable_type = "categorical",
categorical_type = "bars") +
ggtitle("Ceteris-paribus profile", "")
1.3.9.6 Instance Level Exploration : Local Fidelity Plots (ILE_LFP)
Code Chunk | Output
Instance_1_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
neighbours = 50)
plot(Instance_1_GBM_LFP)
Instance_2_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
neighbours = 50)
plot(Instance_2_GBM_LFP)
1.3.9.7 Instance Level Exploration : Local Stability Plots (ILE_LSP)
Code Chunk | Output
Instance_1_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
neighbours = 5,
variables = c("INFMOR","NCOMOR","CLTECH","PERCAP"))
plot(Instance_1_GBM_LFP)
Instance_1_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_1_Philippines_Female[,c(1:6)],
neighbours = 5,
variables = c("GENDER","CONTIN"))
plot(Instance_1_GBM_LFP)
Instance_2_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
neighbours = 5,
variables = c("INFMOR","NCOMOR","CLTECH","PERCAP"))
plot(Instance_2_GBM_LFP)
Instance_2_GBM_LFP <- predict_diagnostics(explainer = GBM_DALEX,
new_observation = Instance_2_Philippines_Male[,c(1:6)],
neighbours = 5,
variables = c("GENDER","CONTIN"))
plot(Instance_2_GBM_LFP)
1.4 Summary
2. References
[Book] Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models With examples in R and Python by Przemyslaw Biecek and Tomasz Burzykowski
[Book] Explainable Machine Learning: A Guide for Making Black Box Models Explainable by Christoph Molnar
[Book] Explainable AI: Interpreting, Explaining and Visualizing Deep Learning by Wojciech Samek, Gregoire Montavon, Andrea Vedaldi, Lars Kai Hansen and Klaus-Robert Muller
[Book] Applied Predictive Modeling by Max Kuhn and Kjell Johnson
[Book] The Elements of Statistical Learning by Trevor Hastie , Robert Tibshirani and Jerome Friedman
[Book] Pattern Recognition and Neural Networks by Brian Ripley
[R Package] DALEX by Przemyslaw Biecek, Szymon Maksymiuk and Hubert Baniecki
[R Package] iml by Christoph Molnar
[R Package] ALEPlot by Dan Apley
[R Package] randomForest by Leo Breiman, Adele Cutler, Andy Liaw and Matthew Wiener
[R Package] auditor by Alicja Gosiewska, Przemyslaw Biecek, Hubert Baniecki and Tomasz Mikołajczyk
[R Package] fastshap by Brandon Greenwell
[R Package] rms by Frank Harrell
[R Package] EIX by Szymon Maksymiuk, Ewelina Karbowiak and Przemyslaw Biecek
[R Package] parsnip by Max Kuhn and Davis Vaughan
[R Package] h2o by Tomas Fryda, Erin LeDell, Navdeep Gill, Spencer Aiello, Anqi Fu, Arno Candel, Cliff Click, Tom Kraljevic, Tomas Nykodym, Patrick Aboyoun, Michal Kurka, Michal Malohlava, Sebastien Poirier and Wendy Wong
[R Package] tidymodels by Max Kuhn and Hadley Wickham
[R Package] e1071 by David Meyer, Evgenia Dimitriadou, Kurt Hornik, Andreas Weingessel and Friedrich Leisch
[R Package] lime by Emil Hvitfeldt, Thomas Lin Pedersen and Michael Benesty
[R Package] ExplainPrediction by Marko Robnik-Sikonja
[R Package] localModel by Przemyslaw Biecek and Mateusz Staniak
[R Package] skimr by Elin Waring
[R Package] corrplot by Taiyun Wei
[R Package] lares by Bernardo Lares
[R Package] minerva by Michele Filosi
[R Package] CORElearn by Marko Robnik-Sikonja and Petr Savicky
[R Package] caret by Max Kuhn
[R Package] gbm by Brandon Greenwell, Bradley Boehmke, Jay Cunningham and GBM Developers
[R Package] randomForest by Andy Liaw
[R Package] nnet by Brian Ripley
[R Package] pls by Kristian Hovde Liland
[R Package] Cubist by Max Kuhn
[R Package] patchwork by Thomas Lin Pedersen
[Article] Interpretation Methods for Black-Box Machine Learning Models in Insurance Rating-Type Applications by Gabe Taylor, Sunish Menon, Huimin Ru, Ray Wright, Xin Hunt and Ralph Abbey
[Article] 4 Model-Agnostic Interpretability Techniques for Complex Models by Funda Gunes
[Article] How Can We Provide Post-Hoc Explanations for Black-Box AI Models? by Joy Lin
[Article] Correlation in R: Pearson and Spearman Correlation Matrix by Daniel Johnson
[Article] Correlation (Pearson, Kendall, Spearman) by Statistics Solutions Team
[Article] A Comparison of the Pearson and Spearman Correlation Methods by Minitab Support Team
[Article] How to Perform Lowess Smoothing in R (Step-by-Step) by Statology Team
[Article] Maximal Information Coefficient by R Bloggers Team
[Article] Methods for Forecasts of Continuous Variables by WWRP/WGNE Joint Working Group on Forecast Verification Research Team
[Article] Generalized Boosting Model by BCCVL Team
[Article] An Introduction to Partial Least Squares by Statology Team
[Article] Random Forest by BCCVL Team
[Article] Artificial Neural Network by BCCVL Team
[Article] Cubist Regression Models by Max Kuhn
[Publication] Robust Locally Weighted Regression and Smoothing Scatterplots by William Cleveland (Journal of the American Statistical Association)
[Publication] Mathematical Contributions to the Theory of Evolution: Regression, Heredity and Panmixia by Karl Pearson (Royal Society)
[Publication] The Proof and Measurement of Association between Two Things by Charles Spearman (The American Journal of Psychology)
[Publication] Detecting Novel Associations in Large Data Sets by David Reshef, Yakir Reshef, Hilary Finucane, Sharon Grossman, Gilean Mcvean, Peter Turnbaugh, Eric Lander, Michael Mitzenmacher and Pardis Sabeti (Science)
[Publication] Stochastic Gradient Boosting by Jerome Friedman (Computational Statistics and Data Analysis)
[Publication] Random Forest by Leo Breiman (Machine Learning)
[Publication] The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses by Svante Wold, Axel Ruhe, Herman Wold, and William Dunn (Society for Industrial and Applied Mathematics)
[Publication] Learning With Continuous Classes by Ross Quinlan (Proceedings of the 5th Australian Joint Conference On Artificial Intelligence)
[Publication] A Survey of Methods for Explaining Black Box Models by Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti and Dino Pedreschi (ACM Computing Surveys)
[Publication] iml: An R package for Interpretable Machine Learning by Christoph Molnar (Journal of Open Source Software)
[Publication] All Models Are Wrong, but Many Are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously by Aaron Fisher, Cynthia Rudin and Francesca Dominici (Journal of Machine Learning Research)
[Publication] Greedy Function Approximation: A Gradient Boosting Machine by Jerome Friedman (Annals of Statistics)
[Publication] Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation by Alex Goldstein, Adam Kapelner, Justin Bleich and Emil Pitkin (Journal of Computational and Graphical Statistics)
[Publication] An Efficient Explanation of Individual Classifications Using Game Theory by Erik Strumbelj and Igor Kononenko (Journal of Machine Learning Research)
[Publication] Explaining Classifications For Individual Instances by Marco Robnik-Sikonja and Igor Kononenko (IEEE Transactions on Knowledge and Data Engineering)